From f5a19af10a4a79b5cb096c46311ebb6ca4129274 Mon Sep 17 00:00:00 2001 From: Vratko Polak Date: Wed, 28 Aug 2024 10:40:08 +0200 Subject: feat(ietf): Prepare for MLRsearch draft-08 Change-Id: I882e7068b1dad4547356a497353abc9dc30abffd Signed-off-by: Vratko Polak --- docs/ietf/draft-ietf-bmwg-mlrsearch-07.md | 3123 --------------------------- docs/ietf/draft-ietf-bmwg-mlrsearch-07.txt | 2800 ------------------------- docs/ietf/draft-ietf-bmwg-mlrsearch-07.xml | 3136 ---------------------------- docs/ietf/draft-ietf-bmwg-mlrsearch-08.md | 3123 +++++++++++++++++++++++++++ 4 files changed, 3123 insertions(+), 9059 deletions(-) delete mode 100644 docs/ietf/draft-ietf-bmwg-mlrsearch-07.md delete mode 100644 docs/ietf/draft-ietf-bmwg-mlrsearch-07.txt delete mode 100644 docs/ietf/draft-ietf-bmwg-mlrsearch-07.xml create mode 100644 docs/ietf/draft-ietf-bmwg-mlrsearch-08.md (limited to 'docs/ietf') diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-07.md deleted file mode 100644 index eb2a218bb8..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.md +++ /dev/null @@ -1,3123 +0,0 @@ ---- - -title: Multiple Loss Ratio Search -abbrev: MLRsearch -docname: draft-ietf-bmwg-mlrsearch-07 -date: 2024-07-18 - -ipr: trust200902 -area: ops -wg: Benchmarking Working Group -kw: Internet-Draft -cat: info - -coding: us-ascii -pi: # can use array (if all yes) or hash here - toc: yes - sortrefs: # defaults to yes - symrefs: yes - -author: - - - ins: M. Konstantynowicz - name: Maciek Konstantynowicz - org: Cisco Systems - email: mkonstan@cisco.com - - - ins: V. Polak - name: Vratko Polak - org: Cisco Systems - email: vrpolak@cisco.com - -normative: - RFC1242: - RFC2285: - RFC2544: - RFC8219: - RFC9004: - -informative: - TST009: - target: https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.04.01_60/gs_NFV-TST009v030401p.pdf - title: "TST 009" - FDio-CSIT-MLRsearch: - target: https://csit.fd.io/cdocs/methodology/measurements/data_plane_throughput/mlr_search/ - title: "FD.io CSIT Test Methodology - MLRsearch" - date: 2023-10 - PyPI-MLRsearch: - target: https://pypi.org/project/MLRsearch/1.2.1/ - title: "MLRsearch 1.2.1, Python Package Index" - date: 2023-10 - ---- abstract - -This document proposes extensions to [RFC2544] throughput search by -defining a new methodology called Multiple Loss Ratio search -(MLRsearch). MLRsearch aims to minimize search duration, -support multiple loss ratio searches, -and enhance result repeatability and comparability. - -The primary reason for extending [RFC2544] is to address the challenges -and requirements presented by the evaluation and testing -of software-based networking systems' data planes. - -To give users more freedom, MLRsearch provides additional configuration options -such as allowing multiple short trials per load instead of one large trial, -tolerating a certain percentage of trial results with higher loss, -and supporting the search for multiple goals with varying loss ratios. - ---- middle - -{::comment} - - As we use Kramdown to convert from Markdown, - we use this way of marking comments not to be visible in the rendered draft. - https://stackoverflow.com/a/42323390 - If another engine is used, convert to this way: - https://stackoverflow.com/a/20885980 - - [toc] - -{:/comment} - - -# Purpose and Scope - -The purpose of this document is to describe Multiple Loss Ratio search -(MLRsearch), a data plane throughput search methodology optimized for software -networking DUTs. - -Applying vanilla [RFC2544] throughput bisection to software DUTs -results in several problems: - -- Binary search takes too long as most trials are done far from the - eventually found throughput. -- The required final trial duration and pauses between trials - prolong the overall search duration. -- Software DUTs show noisy trial results, - leading to a big spread of possible discovered throughput values. -- Throughput requires a loss of exactly zero frames, but the industry - frequently allows for small but non-zero losses. -- The definition of throughput is not clear when trial results are inconsistent. - -To address the problems mentioned above, -the MLRsearch test methodology specification employs the following enhancements: - -- Allow multiple short trials instead of one big trial per load. - - Optionally, tolerate a percentage of trial results with higher loss. -- Allow searching for multiple Search Goals, with differing loss ratios. - - Any trial result can affect each Search Goal in principle. -- Insert multiple coarse targets for each Search Goal, earlier ones need - to spend less time on trials. - - Earlier targets also aim for lesser precision. - - Use Forwarding Rate (FR) at maximum offered load - [RFC2285] (section 3.6.2) to initialize the initial targets. -- Take care when dealing with inconsistent trial results. - - Reported throughput is smaller than the smallest load with high loss. - - Smaller load candidates are measured first. -- Apply several load selection heuristics to save even more time - by trying hard to avoid unnecessarily narrow bounds. - -Some of these enhancements are formalized as MLRsearch specification, -the remaining enhancements are treated as implementation details, -thus achieving high comparability without limiting future improvements. - -MLRsearch configuration options are flexible enough to -support both conservative settings and aggressive settings. -The conservative settings lead to results -unconditionally compliant with [RFC2544], -but longer search duration and worse repeatability. -Conversely, aggressive settings lead to shorter search duration -and better repeatability, but the results are not compliant with [RFC2544]. - -No part of [RFC2544] is intended to be obsoleted by this document. - -# Identified Problems - -This chapter describes the problems affecting usability -of various performance testing methodologies, -mainly a binary search for [RFC2544] unconditionally compliant throughput. - -## Long Search Duration - -{::comment} - [Low priority] - - MKP2 [VP] TODO: Look for mentions of search duration in existing RFCs. - - MKP2 [VP] TODO: If not found, define right after defining "the search". - -{:/comment} - -The emergence of software DUTs, with frequent software updates and a -number of different frame processing modes and configurations, -has increased both the number of performance tests -required to verify the DUT update and the frequency of running those tests. -This makes the overall test execution time even more important than before. - -The current [RFC2544] throughput definition restricts the potential -for time-efficiency improvements. -A more generalized throughput concept could enable further enhancements -while maintaining the precision of simpler methods. - -The bisection method, when unconditionally compliant with [RFC2544], -is excessively slow. -This is because a significant amount of time is spent on trials -with loads that, in retrospect, are far from the final determined throughput. - -[RFC2544] does not specify any stopping condition for throughput search, -so users already have an access to a limited trade-off -between search duration and achieved precision. -However, each full 60-second trials doubles the precision, -so not many trials can be removed without a substantial loss of precision. - -## DUT in SUT - -[RFC2285] defines: -- DUT as - - The network forwarding device to which stimulus is offered and - response measured [RFC2285] (section 3.1.1). -- SUT as - - The collective set of network devices to which stimulus is offered - as a single entity and response measured [RFC2285] (section 3.1.2). - -[RFC2544] specifies a test setup with an external tester stimulating the -networking system, treating it either as a single DUT, or as a system -of devices, an SUT. - -In the case of software networking, the SUT consists of not only the DUT -as a software program processing frames, but also of -server hardware and operating system functions, -with that server hardware resources shared across all programs including -the operating system. - -Given that the SUT is a shared multi-tenant environment -encompassing the DUT and other components, the DUT might inadvertently -experience interference from the operating system -or other software operating on the same server. - -Some of this interference can be mitigated. -For instance, -pinning DUT program threads to specific CPU cores -and isolating those cores can prevent context switching. - -Despite taking all feasible precautions, some adverse effects may still impact -the DUT's network performance. -In this document, these effects are collectively -referred to as SUT noise, even if the effects are not as unpredictable -as what other engineering disciplines call noise. - -DUT can also exhibit fluctuating performance itself, for reasons -not related to the rest of SUT. For example due to pauses in execution -as needed for internal stateful processing. -In many cases this -may be an expected per-design behavior, as it would be observable even -in a hypothetical scenario where all sources of SUT noise are eliminated. -Such behavior affects trial results in a way similar to SUT noise. -As the two phenomenons are hard to distinguish, -in this document the term 'noise' is used to encompass -both the internal performance fluctuations of the DUT -and the genuine noise of the SUT. - -A simple model of SUT performance consists of an idealized noiseless performance, -and additional noise effects. -For a specific SUT, the noiseless performance is assumed to be constant, -with all observed performance variations being attributed to noise. -The impact of the noise can vary in time, sometimes wildly, -even within a single trial. -The noise can sometimes be negligible, but frequently -it lowers the observed SUT performance as observed in trial results. - -In this model, SUT does not have a single performance value, it has a spectrum. -One end of the spectrum is the idealized noiseless performance value, -the other end can be called a noiseful performance. -In practice, trial result -close to the noiseful end of the spectrum happens only rarely. -The worse the performance value is, the more rarely it is seen in a trial. -Therefore, the extreme noiseful end of the SUT spectrum is not observable -among trial results. -Also, the extreme noiseless end of the SUT spectrum -is unlikely to be observable, this time because some small noise effects -are likely to occur multiple times during a trial. - -Unless specified otherwise, this document's focus is -on the potentially observable ends of the SUT performance spectrum, -as opposed to the extreme ones. - -When focusing on the DUT, the benchmarking effort should ideally aim -to eliminate only the SUT noise from SUT measurements. -However, -this is currently not feasible in practice, as there are no realistic enough -models available to distinguish SUT noise from DUT fluctuations, -based on authors' experience and available literature. - -Assuming a well-constructed SUT, the DUT is likely its -primary performance bottleneck. -In this case, we can define the DUT's -ideal noiseless performance as the noiseless end of the SUT performance spectrum, -especially for throughput. -However, other performance metrics, such as latency, -may require additional considerations. - -Note that by this definition, DUT noiseless performance -also minimizes the impact of DUT fluctuations, as much as realistically possible -for a given trial duration. - -MLRsearch methodology aims to solve the DUT in SUT problem -by estimating the noiseless end of the SUT performance spectrum -using a limited number of trial results. - -Any improvements to the throughput search algorithm, aimed at better -dealing with software networking SUT and DUT setup, should employ -strategies recognizing the presence of SUT noise, allowing the discovery of -(proxies for) DUT noiseless performance -at different levels of sensitivity to SUT noise. - -## Repeatability and Comparability - -[RFC2544] does not suggest to repeat throughput search. -And from just one -discovered throughput value, it cannot be determined how repeatable that value is. -Poor repeatability then leads to poor comparability, -as different benchmarking teams may obtain varying throughput values -for the same SUT, exceeding the expected differences from search precision. - -[RFC2544] throughput requirements (60 seconds trial and -no tolerance of a single frame loss) affect the throughput results -in the following way. -The SUT behavior close to the noiseful end of its performance spectrum -consists of rare occasions of significantly low performance, -but the long trial duration makes those occasions not so rare on the trial level. -Therefore, the binary search results tend to wander away from the noiseless end -of SUT performance spectrum, more frequently and more widely than short -trials would, thus causing poor throughput repeatability. - -The repeatability problem can be addressed by defining a search procedure -that identifies a consistent level of performance, -even if it does not meet the strict definition of throughput in [RFC2544]. - -According to the SUT performance spectrum model, better repeatability -will be at the noiseless end of the spectrum. -Therefore, solutions to the DUT in SUT problem -will help also with the repeatability problem. - -Conversely, any alteration to [RFC2544] throughput search -that improves repeatability should be considered -as less dependent on the SUT noise. - -An alternative option is to simply run a search multiple times, and report some -statistics (e.g. average and standard deviation). -This can be used -for a subset of tests deemed more important, -but it makes the search duration problem even more pronounced. - -## Throughput with Non-Zero Loss - -[RFC1242] (section 3.17 Throughput) defines throughput as: - The maximum rate at which none of the offered frames - are dropped by the device. - -Then, it says: - Since even the loss of one frame in a - data stream can cause significant delays while - waiting for the higher level protocols to time out, - it is useful to know the actual maximum data - rate that the device can support. - -However, many benchmarking teams accept a small, -non-zero loss ratio as the goal for their load search. - -Motivations are many: - -- Modern protocols tolerate frame loss better, - compared to the time when [RFC1242] and [RFC2544] were specified. - -- Trials nowadays send way more frames within the same duration, - increasing the chance of a small SUT performance fluctuation - being enough to cause frame loss. - -- Small bursts of frame loss caused by noise have otherwise smaller impact - on the average frame loss ratio observed in the trial, - as during other parts of the same trial the SUT may work more closely - to its noiseless performance, thus perhaps lowering the Trial Loss Ratio - below the Goal Loss Ratio value. - -- If an approximation of the SUT noise impact on the Trial Loss Ratio is known, - it can be set as the Goal Loss Ratio. - -Regardless of the validity of all similar motivations, -support for non-zero loss goals makes any search algorithm more user-friendly. -[RFC2544] throughput is not user-friendly in this regard. - -Furthermore, allowing users to specify multiple loss ratio values, -and enabling a single search to find all relevant bounds, -significantly enhances the usefulness of the search algorithm. - -Searching for multiple Search Goals also helps to describe the SUT performance -spectrum better than the result of a single Search Goal. -For example, the repeated wide gap between zero and non-zero loss loads -indicates the noise has a large impact on the observed performance, -which is not evident from a single goal load search procedure result. - -It is easy to modify the vanilla bisection to find a lower bound -for the intended load that satisfies a non-zero Goal Loss Ratio. -But it is not that obvious how to search for multiple goals at once, -hence the support for multiple Search Goals remains a problem. - -## Inconsistent Trial Results - -While performing throughput search by executing a sequence of -measurement trials, there is a risk of encountering inconsistencies -between trial results. - -The plain bisection never encounters inconsistent trials. -But [RFC2544] hints about the possibility of inconsistent trial results, -in two places in its text. -The first place is section 24, where full trial durations are required, -presumably because they can be inconsistent with the results -from short trial durations. -The second place is section 26.3, where two successive zero-loss trials -are recommended, presumably because after one zero-loss trial -there can be a subsequent inconsistent non-zero-loss trial. - -Examples include: - -- A trial at the same load (same or different trial duration) results - in a different Trial Loss Ratio. -- A trial at a higher load (same or different trial duration) results - in a smaller Trial Loss Ratio. - -Any robust throughput search algorithm needs to decide how to continue -the search in the presence of such inconsistencies. -Definitions of throughput in [RFC1242] and [RFC2544] are not specific enough -to imply a unique way of handling such inconsistencies. - -Ideally, there will be a definition of a new quantity which both generalizes -throughput for non-zero-loss (and other possible repeatability enhancements), -while being precise enough to force a specific way to resolve trial result -inconsistencies. -But until such a definition is agreed upon, the correct way to handle -inconsistent trial results remains an open problem. - -# MLRsearch Specification - -This section describes MLRsearch specification including all technical -definitions needed for evaluating whether a particular test procedure -complies with MLRsearch specification. - -{::comment} - [Good idea for 08, maybe ask BMWG first?] - - TODO VP: Separate Requirements and Recommendations/Suggestions - paragraphs? (currently requirements are in discussion subsections - - discussion should only clarify things without adding new - requirements) - -{:/comment} - -## Overview - -MLRsearch specification describes a set of abstract system components, -acting as functions with specified inputs and outputs. - -A test procedure is said to comply with MLRsearch specification -if it can be conceptually divided into analogous components, -each satisfying requirements for the corresponding MLRsearch component. - -The Measurer component is tasked to perform trials, -the Controller component is tasked to select trial loads and durations, -the Manager component is tasked to pre-configure everything -and to produce the test report. -The test report explicitly states Search Goals (as the Controller Inputs) -and corresponding Goal Results (Controller Outputs). - -{::comment} - [Low priority] - - MKP2 TODO: Find a good reference for the test report, seems only implicit in RFC2544. - -{:/comment} - -The Manager calls the Controller once, -the Controller keeps calling the Measurer -until all stopping conditions are met. - -The part where Controller calls the Measurer is called the search. -Any activity done by the Manager before it calls the Controller -(or after Controller returns) is not considered to be part of the search. - -MLRsearch specification prescribes regular search results and recommends -their stopping conditions. Irregular search results are also allowed, -they may have different requirements and stopping conditions. - -Search results are based on load classification. -When measured enough, any chosen load either achieves of fails each search goal, -thus becoming a lower or an upper bound for that goal. -When the relevant bounds are at loads that are close enough -(according to goal precision), the regular result is found. -Search stops when all regular results are found -(or if some goals are proven to have only irregular results). - -## Measurement Quantities - -MLRsearch specification uses a number of measurement quantities. - -In general, MLRsearch specification does not require particular units to be used, -but it is REQUIRED for the test report to state all the units. -For example, ratio quantities can be dimensionless numbers between zero and one, -but may be expressed as percentages instead. - -For convenience, a group of quantities can be treated as a composite quantity, -One constituent of a composite quantity is called an attribute, -and a group of attribute values is called an instance of that composite quantity. - -Some attributes are not independent from others, -and they can be calculated from other attributes. -Such quantites are called derived quantities. - -## Existing Terms - -RFC 1242 "Benchmarking Terminology for Network Interconnect Devices" -contains basic definitions, and -RFC 2544 "Benchmarking Methodology for Network Interconnect Devices" -contains discussions of a number of terms and additional methodology requirements. -RFC 2285 adds more terms and discussions, describing some known situations -in more precise way. - -All three documents should be consulted -before attempting to make use of this document. - -Definitions of some central terms are copied and discussed in subsections. - -{::comment} - [Good idea for 08, but needs more work. Ask BMWG?] - - Alternatively, quick list of all (existing and new here) terms, - with links (external or internal respectively) to definitions. - - MKP3 [VP] TODO: Even if the following list will not be in final draft, - it is useful to keep it around (maybe commented-out) while editing. - - MKP3 VP note: rough list of all RFC references: - - [RFC1242] (section 3.17 Throughput) ... definition - - [RFC2544] (section 26.1 Throughput) ... methodology - - [RFC2544] (section 24. Trial duration): - - full trial durations (implies short trials) - - Also 60s for unconditional compliance is here. - - Also "the search" (without quotes) appears there. - - Also "binary search" (with quotes) appears there. - - [RFC2544] (section 26.3 Frame loss rate): - - two successive zero-loss trials are recommended (hints about loss inversion) - - un/conditionally compliant with [RFC2544] - - [RFC2544] (section 26. Benchmarking tests:) - - all its "dot sections" have "Reporting format:" paragraphs - - (implies test report) - - [RFC2544] (section 26.1 Throughput) wants graph, frame size on X axis. - - [RFC2544] (section 23. Trial description) trial - - general description of trial - - wait times specifically, maybe also learning frames? - - Constant Load of [RFC1242] (section 3.4 Constant Load) - - Data Rate of [RFC2544] (section 14. Bidirectional traffic) - - seems equal to input frame rate [RFC2544] (23. Trial description). - - [RFC2544] (section 21. Bursty traffic) suggests non-constant loads? - - Intended Load of [RFC2285] (section 3.5.1 Intended load (Iload)) - - [RFC2285] (Section 3.5.2 Offered load (Oload)) - - Frame Loss Rate of [RFC1242] (section 3.6 Frame Loss Rate) - - Forwarding Rate as defined in [RFC2285] (section 3.6.1 Forwarding rate (FR)) - - [RFC2544] (section 20. Maximum frame rate) - - [RFC2285] (3.5.3 Maximum offered load (MOL)) - - reordered frames [RFC2544] (section 10. Verifying received frames) - - For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, - as recommended from [RFC2544] (section 8. Frame formats) - and [RFC2544] (section 12. Protocol addresses). - - [RFC8219] (section 5.3. Traffic Setup) introduces traffic setups consisting of a mix of IPv4 and IPv6 traffic - - [RFC2544] (section 9. Frame sizes) - - [RFC1242] (section 3.5 Data link frame size) - - [RFC2285] (section 3.6.2) FRMOL - - [RFC2285] (section 3.1.1) DUT - - [RFC2285] (section 3.1.2) SUT - - [RFC2544] (section 6. Test set up) test setup with (an external) tester - - [RFC9004] B2B - - [RFC8219] (section 5.3. Traffic Setup) for an example of ip4+ip6 mixed traffic - - - MKP3 [VP] TODO: Do not mention those that do not need discussion here. - -{:/comment} - - -{::comment} - [Low priority] - - MKP3 [VP] TODO: Do we even need RFC9004? - -{:/comment} - -{::comment} - [I do not understand what I meant. Typos? Probably not important overall.] - - MKP2 [VP] TODO: Even terms that are discussed in this memo, - they perhaps do not need a separate list (just free paragraphs), - in a chapter after MLRsearch specification. - -{:/comment} - -{::comment} - [Important, just not enough time in 07.] - - MKP3 [VP] TODO: Verify that MLRsearch specification does not discuss - meaning of existing terms without quoting their original definition. - -{:/comment} - -### SUT - -Defined in [RFC2285] (section 3.1.2 System Under Test (SUT)) as follows. - -Definition: - -The collective set of network devices to which stimulus is offered -as a single entity and response measured. - -Discussion: - -An SUT consisting of a single network device is also allowed. - -### DUT - -Defined in [RFC2285] (section 3.1.1 Device Under Test (DUT)) as follows. - -Definition: - -The network forwarding device to which stimulus is offered and -response measured. - -Discussion: - -DUT, as a sub-component of SUT, is only indirectly mentioned -in MLRsearch specification, but is of key relevance for its motivation. - -{::comment} - [Could be useful, but not high priority.] - - ### Tester - - MKP3 TODO: Add Definition and Discusion paragraphs - - MKP3 MK note: Bizarre ... i can't find tester definition in - rfc1242, rfc2288 or rfc2544, but will keep looking. If there isn't one, - we need to define one :). - - [VP] TODO: There were some documents distinguishing TG and TA. - -{:/comment} - -### Trial - -A trial is the part of the test described in [RFC2544] (section 23. Trial description). - -Definition: - - A particular test consists of multiple trials. Each trial returns - one piece of information, for example the loss rate at a particular - input frame rate. Each trial consists of a number of phases: - - a) If the DUT is a router, send the routing update to the "input" - port and pause two seconds to be sure that the routing has settled. - - b) Send the "learning frames" to the "output" port and wait 2 - seconds to be sure that the learning has settled. Bridge learning - frames are frames with source addresses that are the same as the - destination addresses used by the test frames. Learning frames for - other protocols are used to prime the address resolution tables in - the DUT. The formats of the learning frame that should be used are - shown in the Test Frame Formats document. - - c) Run the test trial. - - d) Wait for two seconds for any residual frames to be received. - - e) Wait for at least five seconds for the DUT to restabilize. - -Discussion: - -The definition describes some traits, it is not clear whether all of them -are REQUIRED, or some of them are only RECOMMENDED. - -{::comment} - [Useful if possible.] - - MKP2 [VP] TODO: Search RFCs for better description of "Run the test trial". - -{:/comment} - -For the purposes of the MLRsearch specification, -it is ALLOWED for the test procedure to deviate from the [RFC2544] description, -but any such deviation MUST be made explicit in the test report. - -Trials are the only stimuli the SUT is expected to experience -during the search. - -In some discussion paragraphs, it is useful to consider the traffic -as sent and received by a tester, as implicitly defined -in [RFC2544] (section 6. Test set up). - -An example of deviation from [RFC2544] is using shorter wait times. - -## Trial Terms - -This section defines new and redefine existing terms for quantities -relevant as inputs or outputs of trial, as used by the Measurer component. - -### Trial Duration - -Definition: - -Trial duration is the intended duration of the traffic for a trial. - -Discussion: - -In general, this quantity does not include any preparation nor waiting -described in section 23 of [RFC2544] (section 23. Trial description). - -While any positive real value may be provided, some Measurer implementations -MAY limit possible values, e.g. by rounding down to neared integer in seconds. -In that case, it is RECOMMENDED to give such inputs to the Controller -so the Controller only proposes the accepted values. -Alternatively, the test report MUST present the rounded values -as Search Goal attributes. - -### Trial Load - -Definition: - -The trial load is the intended load for a trial - -Discussion: - -For test report purposes, it is assumed that this is a constant load by default. -This MAY be only an average load, e.g. when the traffic is intended to be busty, -e.g. as suggested in [RFC2544] (section 21. Bursty traffic), -but the test report MUST explicitly mention how non-constant the traffic is. - -Trial load is the quantity defined as Constant Load of [RFC1242] -(section 3.4 Constant Load), Data Rate of [RFC2544] -(section 14. Bidirectional traffic) -and Intended Load of [RFC2285] (section 3.5.1 Intended load (Iload)). -All three definitions specify -that this value applies to one (input or output) interface. - -{::comment} - [Not important.] - - MKP2 [VP] TODO: Also mention input frame rate [RFC2544] (23. Trial description). - -{:/comment} - -For test report purposes, multi-interface aggregate load MAY be reported, -this is understood as the same quantity expressed using different units. -From the report it MUST be clear whether a particular trial load value -is per one interface, or an aggregate over all interfaces. - -Similarly to trial duration, some Measurers may limit the possible values -of trial load. Contrary to trial duration, the test report is NOT REQUIRED -to document such behavior. - -{::comment} - [Can of worms. Be aware, but probably do not let spill into draft.] - - MKP2 [VP] TODO: Why? In practice the difference is small, but what if it is big? - Do we need Trial Effective Load for bounds an conditional throughput purposes? - Should the Controller be recommended to chose load values that are exactly accepted? - - -{:/comment} - -It is ALLOWED to combine trial load and trial duration in a way -that would not be possible to achieve using any integer number of data frames. - -{::comment} - [I feel this is important, to be discussed separately (not in-scope).] - - MKP2 [VP] TODO: Explain why are we not using Oload. - 1. MLRsearch implementations cannot react correctly to big differences - between Iload and Oload. - 2. The media between the tested and the DUT are thus considered to be part of SUT. - If DUT causes congestion control, it is not expected to handle Iload. - - - See further discussion in [Trial Forwarding Ratio] (#Trial-Forwarding-Ratio) - and in [Measurer] (#Measurer) sections for other related issues. - - MKP2 [VP] TODO: Create a separate subsection for Oload discussion, - or clearly separate which aspects are discussed under which term. - - MKP2 [VP] TODO: New idea. Compare the tester to an ordinary router - in some datacenter. The Intended Load is not jst some abstract input. - It is the real traffic coming from routers next hop farther. - It does not matter that DUT has forwarded each frame it received, - if the tester was unable to sent all the traffic in time. - Endpoint see packet loss, they do not care about [RFC2285] - half-duplex, spanning trees, nor congestion control mechanisms. - Formally speaking, I consider even the sending interface of the sender - to be the part of SUT. - Reading [RFC2285] (section 3.5.3 Maximum offered load (MOL)) - "This will be the case when an external source lacks the resources - to transmit frames at the minimum legal inter-frame gap" - that means TRex workers are also part of SUT. If they do not have - enough CPU power to generate frames are required, those frames are lost. - - - MKP2 [VP] TODO: That new idea warants some discussion in "DUT within SUT", - as it is just another case of ther rest of SUT ruining - otherwise good DUT performance. - -{:/comment} - -### Trial Input - -Definition: - -Trial Input is a composite quantity, consisting of two attributes: -trial duration and trial load. - -Discussion: - -When talking about multiple trials, it is common to say "Trial Inputs" -to denote all corresponding Trial Input instances. - -A Trial Input instance acts as the input for one call of the Measurer component. - -Contrary to other composite quantities, MLRsearch implementations -are NOT ALLOWED to add optional attributes here. -This improves interoperability between various implementations of -the Controller and the Measurer. - -### Traffic Profile - -Definition: - -Traffic profile is a composite quantity -containing attributes other than trial load and trial duration, -needed for unique determination of the trial to be performed. - -Discussion: - -All its attributes are assumed to be constant during the search, -and the composite is configured on the Measurer by the Manager -before the search starts. -This is why the traffic profile is not part of the Trial Input. - -As a consequence, implementations of the Manager and the Measurer -must be aware of their common set of capabilities, so that the traffic profile -uniquely defines the traffic during the search. -The important fact is that none of those capabilities -have to be known by the Controller implementations. - -The traffic profile SHOULD contain some specific quantities, -for example [RFC2544] (section 9. Frame sizes) governs -data link frame size as defined in [RFC1242] (section 3.5 Data link frame size). - -Several more specific quantities may be RECOMMENDED, depending on media type. -For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, -as recommended from [RFC2544] (section 8. Frame formats) -and [RFC2544] (section 12. Protocol addresses). - -Depending on SUT configuration, e.g. when testing specific protocols, -additional attributes MUST be included in the traffic profile -and in the test report. - -Example: [RFC8219] (section 5.3. Traffic Setup) introduces traffic setups -consisting of a mix of IPv4 and IPv6 traffic - the implied traffic profile -therefore must include an attribute for their percentage. - -Other traffic properties that need to be somehow specified -in Traffic Profile include: -[RFC2544] (section 14. Bidirectional traffic), -[RFC2285] (section 3.3.3 Fully meshed traffic), -and [RFC2544] (section 11. Modifiers). - -### Trial Forwarding Ratio - -Definition: - -The trial forwarding ratio is a dimensionless floating point value. -It MUST range between 0.0 and 1.0, both inclusive. -It is calculated by dividing the number of frames -successfully forwarded by the SUT -by the total number of frames expected to be forwarded during the trial - -Discussion: - -For most traffic profiles, "expected to be forwarded" means -"intended to get transmitted from Tester towards SUT". - -Trial forwarding ratio MAY be expressed in other units -(e.g. as a percentage) in the test report. - -Note that, contrary to loads, frame counts used to compute -trial forwarding ratio are aggregates over all SUT output interfaces. - -Questions around what is the correct number of frames -that should have been forwarded -is generally outside of the scope of this document. - -{::comment} - [Part two of iload/oload discussion.] - - See discussion in [Measurer] (#Measurer) section - for more details about calibrating test equipment. - - MKP2 [VP] TODO: Define unsent frames? - - MKP2 [VP] TODO: If Oload is fairly below Iload, the unsent frames - should be counted as lost, otherwise search outputs are misleading. - But what is "fairly"? CSIT tolerates 10 microseconds worth of unsent frames. - -{:/comment} - -{::comment} - [Low priority, but maybe useful for somebody?] - - MKP2 [VP] TODO: Mention traffic profiles with uneven frame counts? - E.g. when SUT is expected to perform IP packet fragmentation or reassembly. - - -{:/comment} - -### Trial Loss Ratio - -Definition: - -The Trial Loss Ratio is equal to one minus the trial forwarding ratio. - -Discussion: - -100% minus the trial forwarding ratio, when expressed as a percentage. - -This is almost identical to Frame Loss Rate of [RFC1242] -(section 3.6 Frame Loss Rate), -the only minor difference is that Trial Loss Ratio -does not need to be expressed as a percentage. - -### Trial Forwarding Rate - -Definition: - -The trial forwarding rate is a derived quantity, calculated by -multiplying the trial load by the trial forwarding ratio. - -Discussion: - -It is important to note that while similar, this quantity is not identical -to the Forwarding Rate as defined in [RFC2285] -(section 3.6.1 Forwarding rate (FR)). -The latter is specific to one output interface only, -whereas the trial forwarding ratio is based -on frame counts aggregated over all SUT output interfaces. - -{::comment} - [Part 3 of iload/oload discussion.] - - MKP2 [VP] TODO: If some unsent frames were tolerated (not counted as lost), - this value is actually higher than the real fps output of the SUT. - Should we use the real FR as the basis for Conditional Throughput - (instead of this TFR)? That would require additional Trial Output attribute. - - - MKP2 [VP] TODO: What about duration stretching? - This also causes difference between Iload and Oload, - but in an invisible way. - - MKP2 [VP] TODO: Recommend start+sleep+stop? - How long wait for late frames? RFC2544 2s is too much even at 30s trial. - -{:/comment} - -### Trial Effective Duration - -Definition: - -Trial effective duration is a time quantity related to the trial, -by default equal to the trial duration. - -Discussion: - -This is an optional feature. -If the Measurer does not return any trial effective duration value, -the Controller MUST use the trial duration value instead. - -Trial effective duration may be any time quantity chosen by the Measurer -to be used for time-based decisions in the Controller. - -The test report MUST explain how the Measurer computes the returned -trial effective duration values, if they are not always -equal to the trial duration. - -This feature can be beneficial for users -who wish to manage the overall search duration, -rather than solely the traffic portion of it. -Simply measure the duration of the whole trial (waits including) -and use that as the trial effective duration. - -Also, this is a way for the Measurer to inform the Controller about -its surprising behavior, for example when rounding the trial duration value. - -{::comment} - [Not very important, but easy and nice recommendation.] - - MKP2 [VP] TODO: Recommend for Measurer to return all trials at relevant bounds, - as that may better inform users when surprisingly small amount of trials - was performed, just because the the trial effective duration values were big. - - MKP2 [VP] TODO: Repeat that this is not here to deal with duration stretching. - -{:/comment} - -### Trial Output - -Definition: - -Trial Output is a composite quantity. The REQUIRED attributes are -Trial Loss Ratio, trial effective duration and trial forwarding rate. - -Discussion: - -When talking about multiple trials, it is common to say "Trial Outputs" -to denote all corresponding Trial Output instances. - -Implementations may provide additional (optional) attributes. -The Controller implementations MUST ignore values of any optional attribute -they are not familiar with, -except when passing Trial Output instance to the Manager. - -Example of an optional attribute: -The aggregate number of frames expected to be forwarded during the trial, -especially if it is not just (a rounded-up value) -implied by trial load and trial duration. - -While [RFC2285] (Section 3.5.2 Offered load (Oload)) -requires the offered load value to be reported for forwarding rate measurements, -it is NOT REQUIRED in MLRsearch specification. - -{::comment} - [Side tangent from iload/oload discussion. Stilll recommendation is not obvious.] - - MKP2 TODO: Why? Just because bound trial results are optional in Controller Output? - - MKP2 mk edit note: we need to more explicitly address - the relevance or irrelevance of [RFC2285] (Section 3.5.2 Offered load (Oload)). - Current text in [Trial Load] (#Trial-Load) is ambiguous - quoted below. - - MKP2 "Questions around what is the correct number of frames that should - have been forwarded is generally outside of the scope of this document. - See discussion in [Measurer] (#Measurer) section for more details about - calibrating test equipment." - -{:/comment} - -### Trial Result - -Definition: - -Trial result is a composite quantity, -consisting of the Trial Input and the Trial Output. - -Discussion: - -When talking about multiple trials, it is common to say "trial results" -to denote all corresponding trial result instances. - -While implementations SHOULD NOT include additional attributes -with independent values, they MAY include derived quantities. - -## Goal Terms - -This section defines new and redefine existing terms for quantities -indirectly relevant for inputs or outputs of the Controller component. - -Several goal attributes are defined before introducing -the main component quantity: the Search Goal. - -### Goal Final Trial Duration - -Definition: - -A threshold value for trial durations. - -Discussion: - -This attribute value MUST be positive. - -A trial with Trial Duration at least as long as the Goal Final Trial Duration -is called a full-length trial (with respect to the given Search Goal). - -A trial that is not full-length is called a short trial. - -Informally, while MLRsearch is allowed to perform short trials, -the results from such short trials have only limited impact on search results. - -One trial may be full-length for some Search Goals, but not for others. - -The full relation of this goal to Controller Output is defined later in -this document in subsections of [Goal Result] (#Goal-Result). -For example, the Conditional Throughput for this goal is computed only from -full-length trial results. - -### Goal Duration Sum - -Definition: - -A threshold value for a particular sum of trial effective durations. - -Discussion: - -This attribute value MUST be positive. - -Informally, even when looking only at full-length trials, -MLRsearch may spend up to this time measuring the same load value. - -If the Goal Duration Sum is larger than the Goal Final Trial Duration, -multiple full-length trials may need to be performed at the same load. - -See [TST009 Example] (#TST009-Example) for an example where possibility -of multiple full-length trials at the same load is intended. - -A Goal Duration Sum value lower than the Goal Final Trial Duration -(of the same goal) could save some search time, but is NOT RECOMMENDED. -See [Relevant Upper Bound] (#Relevant-Upper-Bound) for partial explanation. - -### Goal Loss Ratio - -Definition: - -A threshold value for Trial Loss Ratios. - -Discussion: - -Attribute value MUST be non-negative and smaller than one. - -A trial with Trial Loss Ratio larger than a Goal Loss Ratio value -is called a lossy trial, with respect to given Search Goal. - -Informally, if a load causes too many lossy trials, -the Relevant Lower Bound for this goal will be smaller than that load. - -If a trial is not lossy, it is called a low-loss trial, -or (specifically for zero Goal Loss Ratio value) zero-loss trial. - -### Goal Exceed Ratio - -Definition: - -A threshold value for a particular ratio of sums of Trial Effective Durations. - -Discussion: - -Attribute value MUST be non-negative and smaller than one. - -See later sections for details on which sums. -Specifically, the direct usage is only in -[Appendix A: Load Classification] (#Appendix-A\:-Load-Classification) -and [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput). -The impact of that usage is discussed in subsections leading to -[Goal Result] (#Goal-Result). - -Informally, the impact of lossy trials is controlled by this value. -Effectively, Goal Exceed Ratio is a percentage of full-length trials -that may be lossy without the load being classified -as the [Relevant Upper Bound] (#Relevant-Upper-Bound). - -### Goal Width - -Definition: - -A value used as a threshold for deciding -whether two trial load values are close enough. - -Discussion: - -If present, the value MUST be positive. - -Informally, this acts as a stopping condition, -controlling the precision of the search. -The search stops if every goal has reached its precision. - -Implementations without this attribute -MUST give the Controller other ways to control the search stopping conditions. - -Absolute load difference and relative load difference are two popular choices, -but implementations may choose a different way to specify width. - -The test report MUST make it clear what specific quantity is used as Goal Width. - -It is RECOMMENDED to set the Goal Width (as relative difference) value -to a value no smaller than the Goal Loss Ratio. -(The reason is not obvious, see [Throughput] (#Throughput) if interested.) - -### Search Goal - -Definition: - -The Search Goal is a composite quantity consisting of several attributes, -some of them are required. - -Required attributes: -- Goal Final Trial Duration -- Goal Duration Sum -- Goal Loss Ratio -- Goal Exceed Ratio - -Optional attribute: -- Goal Width - -Discussion: - -Implementations MAY add their own attributes. -Those additional attributes may be required by the implementation -even if they are not required by MLRsearch specification. -But it is RECOMMENDED for those implementations -to support missing values by computing reasonable defaults. - -The meaning of listed attributes is formally given only by their indirect effect -on the search results. - -Informally, later sections provide additional intuitions and examples -of the Search Goal attribute values. - -An example of additional attributes required by some implementations -is Goal Initial Trial Duration, together with another attribute -that controls possible intermediate Trial Duration values. -The reasonable default in this case is using the Goal Final Trial Duration -and no intermediate values. - -### Controller Input - -Definition: - -Controller Input is a composite quantity -required as an input for the Controller. -The only REQUIRED attribute is a list of Search Goal instances. - -Discussion: - -MLRsearch implementations MAY use additional attributes. -Those additional attributes may be required by the implementation -even if they are not required by MLRsearch specification. - -Formally, the Manager does not apply any Controller configuration -apart from one Controller Input instance. - -For example, Traffic Profile is configured on the Measurer by the Manager -(without explicit assistance of the Controller). - -The order of Search Goal instances in a list SHOULD NOT -have a big impact on Controller Output (see section [Controller Output] (#Controller-Output) , -but MLRsearch implementations MAY base their behavior on the order -of Search Goal instances in a list. - -An example of an optional attribute (outside the list of Search Goals) -required by some implementations is Max Load. -While this is a frequently used configuration parameter, -already governed by [RFC2544] (section 20. Maximum frame rate) -and [RFC2285] (3.5.3 Maximum offered load (MOL)), -some implementations may detect or discover it instead. - -{::comment} - [Not important directly, may matter for iload/oload.] - - MKP2 [VP] TODO: 2544 and 2285 care about half-duplex media. Should we? - -{:/comment} - -{::comment} - [Maybe obvious but I think useful. RFC2544 talks about header compression in WANs.] - - MKP2 [VP] TODO: Mention that Max Load should care about all media within SUT, - including DUT-DUT links. Important when that link carries encapsulated traffic, - as bandwidth limit there implies lower max rate - (than implied by tester-SUT links). - -{:/comment} - -In MLRsearch specification, the [Relevant Upper Bound] (#Relevant-Upper-Bound) -is added as a required attribute precisely because it makes the search result -independent of Max Load value. - -{::comment} - [User recommendation, we should have separate section summarizing those.] - - [VP] TODO for MK: The rest of this subsection is new, review? - - It is RECOMMENDED to use the same Goal Final Trial Duration value across all goals. - Otherwise, some goals may be measured at Trial Durations longer than needed, - with possibly unexpected impacts on repeatability and comparability. - - For example when Goal Loss Ratio is zero, any increase in Trial Duration - also increases the likelihood of the trial to become lossy, - similar to usage of lower Goal Exceed Ratio or larger Goal Duration Sum, - both of which tend to lower the search results, towards noisy end - of performance spectrum. - - Also, it is recommended to avoid "incomparable" goals, e.g. one with - lower loss ratio but higher exceed ratio, and other with higher loss ratio - but lower loss ratio. In worst case, this can make the search to last too long. - Implementations are RECOMMENDED to sort the goals and start with - stricter ones first, as bounds for those will not get invalidated - byt measureing for less trict goal later in the search. - -{:/comment} - -## Search Goal Examples - -### RFC2544 Goal - -The following set of values makes the search result unconditionally compliant -with [RFC2544] (section 24 Trial duration) - -- Goal Final Trial Duration = 60 seconds -- Goal Duration Sum = 60 seconds -- Goal Loss Ratio = 0% -- Goal Exceed Ratio = 0% - -The latter two attributes are enough to make the search goal -conditionally compliant, adding the first attribute -makes it unconditionally compliant. - -The second attribute (Goal Duration Sum) only prevents MLRsearch -from repeating zero-loss full-length trials. - -Non-zero exceed ratio could prolong the search and allow loss inversion -between lower-load lossy short trial and higher-load full-length zero-loss trial. -From [RFC2544] alone, it is not clear whether that higher load -could be considered as compliant throughput. - -### TST009 Goal - -One of the alternatives to RFC2544 is described in -[TST009] (section 12.3.3 Binary search with loss verification). -The idea there is to repeat lossy trials, hoping for zero loss on second try, -so the results are closer to the noiseless end of performance sprectum, -and more repeatable and comparable. - -Only the variant with "z = infinity" is achievable with MLRsearch. - -{::comment} - [Low priority, unless a short sentence is found.] - - MKP2 MK note: Shouldn't we add a note about how MLRsearch goes about - addressing the TST009 point related to z, that is "z is threshold of - Lord(r) to override Loss Verification when the count of lost frames is - very high and unnecessary verification trials."? i.e. by have Goal Loss - Ratio. Thoughts? - -{:/comment} - -For example, for "r = 2" variant, the following search goal should be used: - -- Goal Final Trial Duration = 60 seconds -- Goal Duration Sum = 120 seconds -- Goal Loss Ratio = 0% -- Goal Exceed Ratio = 50% - -If the first 60s trial has zero loss, it is enough for MLRsearch to stop -measuring at that load, as even a second lossy trial -would still fit within the exceed ratio. - -But if the first trial is lossy, MLRsearch needs to perform also -the second trial to classify that load. -As Goal Duration Sum is twice as long as Goal Final Trial Duration, -third full-length trial is never needed. - -## Result Terms - -Before defining the output of the Controller, -it is useful to define what the Goal Result is. - -The Goal Result is a composite quantity. - -Following subsections define its attribute first, before describing the Goal Result quantity. - -There is a correspondence between Search Goals and Goal Results. -Most of the following subsections refer to a given Search Goal, -when defining attributes of the Goal Result. -Conversely, at the end of the search, each Search Goal -has its corresponding Goal Result. - -Conceptually, the search can be seen as a process of load classification, -where the Controller attempts to classify some loads as an Upper Bound -or a Lower Bound with respect to some Search Goal. - -Before defining real attributes of the goal result, -it is useful to define bounds in general. - -### Relevant Upper Bound - -Definition: - -The Relevant Upper Bound is the smallest trial load value that is classified -at the end of the search as an upper bound -(see [Appendix A: Load Classification] (#Appendix-A\:-Load-Classification)) -for the given Search Goal. - -Discussion: - -One search goal can have many different load classified as an upper bound. -At the end of the search, one of those loads will be the smallest, -becoming the relevant upper bound for that goal. - -In more detail, the set of all trial outputs (both short and full-length, -enough of them according to Goal Duration Sum) -performed at that smallest load failed to uphold all the requirements -of the given Search Goal, mainly the Goal Loss Ratio -in combination with the Goal Exceed Ratio. - -{::comment} - [Recheck. Move to end?] - - [VP] TODO: Is the above a good summary of Appendix A inputs? - -{:/comment} - -If Max Load does not cause enough lossy trials, -the Relevant Upper Bound does not exist. -Conversely, if Relevant Upper Bound exists, -it is not affected by Max Load value. - -{::comment} - [Medium priority, depends on how many user recommendations we have.] - - With non-zero exceed ratio values, a lossy short trial may not be enough - to classify a load as the relevant upper bound. - Users MAY apply Goal Duration Sum value lower than Goal Final Trial Duration - to force such classification in hope to save time, - but it is RECOMMENDED not to do so, as in practice - it hurts comparability and repeatability. - -{:/comment} - -{::comment} - [Probably too technical, unless relation to repeatability is found.] - - In general, a load starts as as undecided, then maybe flips to become - an upper bound. MLRsearch stops measuring at that load for this goal, - but it may be forced to measure more for some other search goals, - in which case the load may flip to a lower bound (and back and forth). - - [VP] TODO: Confirm the load can never flip back to being undecided. - - Even though the load classification may change during the search, - the goal results are established at the end of the search. - - If the exceed ratio is zero, an upper bound can never flip; - one lossy trial (even short) is enough to pin the classification. - -{:/comment} - -### Relevant Lower Bound - -Definition: - -The Relevant Lower Bound is the largest trial load value -among those smaller than the Relevant Upper Bound, -that got classified at the end of the search as a lower bound (see -[Appendix A: Load Classification] (#Appendix-A\:-Load-Classification)) -for the given Search Goal. - -Discussion: - -Only among loads smaller that the relevant upper bound, -the largest load becomes the relevant lower bound. -With loss inversion, stricter upper bound matters. - -In more detail, the set of all trial outputs (both short and full-length, -enough of them according to Goal Duration Sum) -performed at that largest load managed to uphold all the requirements -of the given Search Goal, mainly the Goal Loss Ratio -in combination with the Goal Exceed Ratio. - -Is no load had enough low-loss trials, the relevant lower bound -MAY not exist. - -{::comment} - [Min Load us useful for detecting broken SUTs (and latency).] - - [VP] TODO: Mention min load here? - - [VP] TODO: Allow zero as implicit lower bound that needs no trials? - If yes, then probably way earlier than here. - -{:/comment} - -Strictly speaking, if the Relevant Upper Bound does not exist, -the Relevant Lower Bound also does not exist. -In that case, Max Load is classified as a lower bound, -but it is not clear whether a higher lower bound -would be found if the search used a higher Max Load value. - -For a regular Goal Result, the distance between the Relevant Lower Bound -and the Relevant Upper Bound MUST NOT be larger than the Goal Width, -if the implementation offers width as a goal attribute. - -{::comment} - [True but no time to fix properly.] - - mk note: Seemingly broken grammar, - "managed to uphold all requirements", should be followed - by stating what it means. - -{:/comment} - -Searching for anther search goal may cause a loss inversion phenomenon, -where a lower load is classified as an upper bound, -but also a higher load is classified as a lower bound for the same search goal. -The definition of the Relevant Lower Bound ignores such high lower bounds. - -{::comment} - [Compare to similar block in upper bound.] - - In general, a load starts as as undecided, then maybe flips to become - a lower bound. MLRsearch stops measuring at that load for this goal, - but it may be forced to measure more for some other search goals, - in which case the load may flip to an upper bound (and back and forth). - - [VP] TODO: Confirm the load can never flip back to being undecided. - - Even though the load classification may change during the search, - the goal results are established at the end of the search. - - No valid exceed ratio value pins the classification as a lower bound. - -{:/comment} - -### Conditional Throughput - -Definition: - -The Conditional Throughput (see section [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput)) -as evaluated at the Relevant Lower Bound of the given Search Goal -at the end of the search. - -Discussion: - -Informally, this is a typical trial forwarding rate, expected to be seen -at the Relevant Lower Bound of the given Search Goal. - -But frequently it is only a conservative estimate thereof, -as MLRsearch implementations tend to stop gathering more data -as soon as they confirm the value cannot get worse than this estimate -within the Goal Duration Sum. - -This value is RECOMMENDED to be used when evaluating repeatability -and comparability if different MLRsearch implementations. - -{::comment} - [Low priority but useful for comparabuility.] - - [VP] TODO: Add subsection for Trial Results At Relevant Bounds - as an optional attribute of Goal Result. - -{:/comment} - -### Goal Result - -Definition: - -The Goal Result is a composite quantity consisting of several attributes. -Relevant Upper Bound and Relevant Lower Bound are REQUIRED attributes, -Conditional Throughput is a RECOMMENDED attribute. - -Discussion: - -Depending on SUT behavior, it is possible that one or both relevant bounds -do not exist. The goal result instance where the required attribute values exist -is informally called a Regular Goal Result instance, -so we can say some goals reached Irregular Goal Results. - -{::comment} - [Probably delete after last edits re irregular results.] - - MKP2 [VP] TODO: Additional attributes should not be required by the Manager? - Explicitly mention that irregular goal result may support different attributes. - - - MKP2 Implementations are free to define their own specific subtypes - of irregular Goal Results, but the test report MUST mark them clearly - as irregular according to this section. - -{:/comment} - -A typical Irregular Goal Result is when all trials at the Max Load -have zero loss, as the Relevant Upper Bound does not exist in that case. - -It is RECOMMENDED that the test report will display such results appropriately, -although MLRsearch specification does not prescibe how. - -{::comment} - [Useful.] - - MKP2 [VP] TODO: Also allways-fail. Link to bounds to avoid duplication. - -{:/comment} - -Anything else regarging Irregular Goal Results, -including their role in stopping conditions of the search -is outside the scope of this document. - -### Search Result - -Definition: - -The Search Result is a single composite object -that maps each Search Goal instance to a corresponding Goal Result instance. - -Discussion: - -Alternatively, the Search Result can be implemented as an ordered list -of the Goal Result instances, matching the order of Search Goal instances. - -{::comment} - [Low priority, as there is no obvious harm.] - - MKP1 [VP] TODO: Disallow any additional attributes? - -{:/comment} - -The Search Result (as a mapping) -MUST map from all the Search Goal instances present in the Controller Input. - -{::comment} - [Not important.] - - [VP] Postponed: API independence, modularity. - -{:/comment} - -{::comment} - [Not needed?] - - MKP1 [VP] TODO: Short sentence on what to do on irregular goal result. - -{:/comment} - -### Controller Output - -Definition: - -The Controller Output is a composite quantity returned from the Controller -to the Manager at the end of the search. -The Search Result instance is its only REQUIRED attribute. - -Discussion: - -MLRsearch implementation MAY return additional data in the Controller Output. - -{::comment} - [Not needed?] - - MKP1 [VP] TODO: Short sentence on what to do on irregular goal result. - - MKP1 [VP] TODO: Irregular output, e.g. with "max search time exceeded" flag? - -{:/comment} - -## MLRsearch Architecture - -{::comment} - [Meta and irrelevant. Delete after verifying other text is good.] - - MKP2 [VP] TODO: Review the folowing: - This section is about division into components, - so it fits this definition: - "The software architecture of a system represents the design decisions - related to overall system structure and behavior." - Saying "MLRsearch Design" does not make it clear if it is - Vratko designing the MLRsearch specification, - or some other person designing a new MLRsearch implementation using that spec. - - -{:/comment} - -MLRsearch architecture consists of three main system components: -the Manager, the Controller, and the Measurer. - -The architecture also implies the presence of other components, -such as the SUT and the Tester (as a sub-component of the Measurer). - -Protocols of communication between components are generally left unspecified. -For example, when MLRsearch specification mentions "Controller calls Measurer", -it is possible that the Controller notifies the Manager -to call the Measurer indirectly instead. This way the Measurer implementations -can be fully independent from the Controller implementations, -e.g. programmed in different programming languages. - -### Measurer - -Definition: - -The Measurer is an abstract system component -that when called with a [Trial Input] (#Trial-Input) instance, -performs one [Trial] (#Trial), -and returns a [Trial Output] (#Trial-Output) instance. - -Discussion: - -This definition assumes the Measurer is already initialized. -In practice, there may be additional steps before the search, -e.g. when the Manager configures the traffic profile -(either on the Measurer or on its tester sub-component directly) -and performs a warmup (if the tester requires one). - -It is the responsibility of the Measurer implementation to uphold -any requirements and assumptions present in MLRsearch specification, -e.g. trial forwarding ratio not being larger than one. - -Implementers have some freedom. -For example [RFC2544] (section 10. Verifying received frames) -gives some suggestions (but not requirements) related to -duplicated or reordered frames. -Implementations are RECOMMENDED to document their behavior -related to such freedoms in as detailed a way as possible. - -It is RECOMMENDED to benchmark the test equipment first, -e.g. connect sender and receiver directly (without any SUT in the path), -find a load value that guarantees the offered load is not too far -from the intended load, and use that value as the Max Load value. -When testing the real SUT, it is RECOMMENDED to turn any big difference -between the intended load and the offered load into increased Trial Loss Ratio. - -Neither of the two recommendations are made into requirements, -because it is not easy to tell when the difference is big enough, -in a way thay would be dis-entangled from other Measurer freedoms. - -### Controller - -Definition: - -The Controller is an abstract system component -that when called with a Controller Input instance -repeatedly computes Trial Input instance for the Measurer, -obtains corresponding Trial Output instances, -and eventually returns a Controller Output instance. - -Discussion: - -Informally, the Controller has big freedom in selection of Trial Inputs, -and the implementations want to achieve the Search Goals -in the shortest expected time. - -The Controller's role in optimizing the overall search time -distinguishes MLRsearch algorithms from simpler search procedures. - -Informally, each implementation can have different stopping conditions. -Goal Width is only one example. -In practice, implementation details do not matter, -as long as Goal Results are regular. - -### Manager - -Definition: - -The Manager is an abstract system component that is reponsible for -configuring other components, calling the Controller component once, -and for creating the test report following the reporting format as -defined in [RFC2544] (section 26. Benchmarking tests). - -Discussion: - -The Manager initializes the SUT, the Measurer (and the Tester if independent) -with their intended configurations before calling the Controller. - -The Manager does not need to be able to tweak any Search Goal attributes, -but it MUST report all applied attribute values even if not tweaked. - -{::comment} - [Not very important but also should be easy to add.] - - MKP2 [VP] TODO: Is saying "RFC2544" indirectly reporting RFC2544 Goal values? - -{:/comment} - -In principle, there should be a "user" (human or CI) -that "starts" or "calls" the Manager and receives the report. -The Manager MAY be able to be called more than once whis way. - -{::comment} - [Not important, unless anybody else asks.] - - MKP2 The Manager may use the Measurer or other system components - to perform other tests, e.g. back-to-back frames, - as the Controller is only replacing the search from - [RFC2544] (section 26.1 Throughput). - -{:/comment} - -## Implementation Compliance - -Any networking measurement setup where there can be logically delineated system components -and there are components satisfying requirements for the Measurer, -the Controller and the Manager, is considered to be compliant with MLRsearch design. - -These components can be seen as abstractions present in any testing procedure. -For example, there can be a single component acting both -as the Manager and the Controller, but as long as values of required attributes -of Search Goals and Goal Results are visible in the test report, -the Controller Input instance and output instance are implied. - -For example, any setup for conditionally (or unconditionally) -compliant [RFC2544] throughput testing -can be understood as a MLRsearch architecture, -assuming there is enough data to reconstruct the Relevant Upper Bound. - -See [RFC2544 Goal] (#RFC2544-Goal) subsection for equivalent Search Goal. - -Any test procedure that can be understood as (one call to the Manager of) -MLRsearch architecture is said to be compliant with MLRsearch specification. - -# Additional Considerations - -This section focuses on additional considerations, intuitions and motivations -pertaining to MLRsearch methodology. - -{::comment} - [Meta, redundant.] - - MKP2 [VP] TODO: Review the following: - If MLRsearch specification is a product design specification - for MLRsearch implementation, then this chapter talks about - my goals and early attempts at designing the MLRsearch specification. - - -{:/comment} - -## MLRsearch Versions - -The MLRsearch algorithm has been developed in a code-first approach, -a Python library has been created, debugged, used in production -and published in PyPI before the first descriptions -(even informal) were published. - -But the code (and hence the description) was evolving over time. -Multiple versions of the library were used over past several years, -and later code was usually not compatible with earlier descriptions. - -The code in (some version of) MLRsearch library fully determines -the search process (for a given set of configuration parameters), -leaving no space for deviations. - -{::comment} - [Different type of external link, should be in 08.] - - MKP2 mk3 note: any references to library - should have specific reference link. - We have FDio-CSIT-MLRsearch in informative: at the start. Link it. - - -{:/comment} - -{::comment} - [Lesson learned is important, but maybe does not need version history?] - - MKP2 mk edit note: Suggest to remove crossed-out text, as it is - distracting, doesn't bring any value, and recalls multiple versions of - MLRsearch library, without any references. A much more appropriate - approach would be to provide a pointer to MLRsearch code versions in - FD.io that evolved over the years, as an example implementation. But I - would question the value of referring to old previous versions in this - document. It's okay for the blog, but not for IETF specification, - unless there are specific lessons learned that need to be highlighted - to support the specification. - -{:/comment} - -This historic meaning of MLRsearch, as a family -of search algorithm implementations, -leaves plenty of space for future improvements, at the cost -of poor comparability of results of search algoritm implementations. - -{::comment} - [Reckeck after clarifying library/algorithm/implementation/specification mess.] - - mk edit note: If the aim of this sentence is to state that there - could be possibly other approaches to address this problem space, then - I think we are already addressing it in the opening sections discussing - problems, and referring to ETSi TST.009 and opnfv work. If the aim is - to define "MLRsearch" as a completely new class of algorithms for - software network benchmarking, of which this spec is just one example, - then i have a problem with it. This specification is very prescriptive - in the main functional areas to address the problem identified, but - still leaving space for further exploration and innovation as noted - elsewhere in this document. It is not a new class of algorithms. It is - a newly defined methodology to amend RFC2544, to specifically address - identified problems. - -{:/comment} - -There are two competing needs. -There is the need for standardization in areas critical to comparability. -There is also the need to allow flexibility for implementations -to innovate and improve in other areas. -This document defines MLRsearch as a new specification -in a manner that aims to fairly balance both needs. - -## Stopping Conditions - -[RFC2544] prescribes that after performing one trial at a specific offered load, -the next offered load should be larger or smaller, based on frame loss. - -The usual implementation uses binary search. -Here a lossy trial becomes -a new upper bound, a lossless trial becomes a new lower bound. -The span of values between the tightest lower bound -and the tightest upper bound (including both values) forms an interval of possible results, -and after each trial the width of that interval halves. - -Usually the binary search implementation tracks only the two tightest bounds, -simply calling them bounds. -But the old values still remain valid bounds, -just not as tight as the new ones. - -After some number of trials, the tightest lower bound becomes the throughput. -[RFC2544] does not specify when, if ever, should the search stop. - -MLRsearch introduces a concept of [Goal Width] (#Goal-Width). - -The search stops -when the distance between the tightest upper bound and the tightest lower bound -is smaller than a user-configured value, called Goal Width from now on. -In other words, the interval width at the end of the search -has to be no larger than the Goal Width. - -This Goal Width value therefore determines the precision of the result. -Due to the fact that MLRsearch specification requires a particular -structure of the result (see [Trial Result] (#Trial-Result) section), -the result itself does contain enough information to determine its -precision, thus it is not required to report the Goal Width value. - -This allows MLRsearch implementations to use stopping conditions -different from Goal Width. - -## Load Classification - -MLRsearch keeps the basic logic of binary search (tracking tightest bounds, -measuring at the middle), perhaps with minor technical differences. - -MLRsearch algorithm chooses an intended load (as opposed to the offered load), -the interval between bounds does not need to be split -exactly into two equal halves, -and the final reported structure specifies both bounds. - -The biggest difference is that to classify a load -as an upper or lower bound, MLRsearch may need more than one trial -(depending on configuration options) to be performed at the same intended load. - -In consequence, even if a load already does have few trial results, -it still may be classified as undecided, neither a lower bound nor an upper bound. - -An explanation of the classification logic is given in the next section [Logic of Load Classification] (#Logic-of-Load-Classification), -as it heavily relies on other subsections of this section. - -For repeatability and comparability reasons, it is important that -given a set of trial results, all implementations of MLRsearch -classify the load equivalently. - -## Loss Ratios - -Another difference between MLRsearch and [RFC2544] binary search is in the goals of the search. -[RFC2544] has a single goal, -based on classifying full-length trials as either lossless or lossy. - -MLRsearch, as the name suggests, can search for multiple goals, -differing in their loss ratios. -The precise definition of the Goal Loss Ratio will be given later. -The [RFC2544] throughput goal then simply becomes a zero Goal Loss Ratio. -Different goals also may have different Goal Widths. - -A set of trial results for one specific intended load value -can classify the load as an upper bound for some goals, but a lower bound -for some other goals, and undecided for the rest of the goals. - -Therefore, the load classification depends not only on trial results, -but also on the goal. -The overall search procedure becomes more complicated, when -compared to binary search with a single goal, -but most of the complications do not affect the final result, -except for one phenomenon, loss inversion. - -## Loss Inversion - -In [RFC2544] throughput search using bisection, any load with a lossy trial -becomes a hard upper bound, meaning every subsequent trial has a smaller -intended load. - -But in MLRsearch, a load that is classified as an upper bound for one goal -may still be a lower bound for another goal, and due to the other goal -MLRsearch will probably perform trials at even higher loads. -What to do when all such higher load trials happen to have zero loss? -Does it mean the earlier upper bound was not real? -Does it mean the later lossless trials are not considered a lower bound? -Surely we do not want to have an upper bound at a load smaller than a lower bound. - -MLRsearch is conservative in these situations. -The upper bound is considered real, and the lossless trials at higher loads -are considered to be a coincidence, at least when computing the final result. - -This is formalized using new notions, the [Relevant Upper Bound] (#Relevant-Upper-Bound) and -the [Relevant Lower Bound] (#Relevant-Lower-Bound). -Load classification is still based just on the set of trial results -at a given intended load (trials at other loads are ignored), -making it possible to have a lower load classified as an upper bound, -and a higher load classified as a lower bound (for the same goal). -The Relevant Upper Bound (for a goal) is the smallest load classified -as an upper bound. -But the Relevant Lower Bound is not simply -the largest among lower bounds. -It is the largest load among loads -that are lower bounds while also being smaller than the Relevant Upper Bound. - -With these definitions, the Relevant Lower Bound is always smaller -than the Relevant Upper Bound (if both exist), and the two relevant bounds -are used analogously as the two tightest bounds in the binary search. -When they are less than the Goal Width apart, -the relevant bounds are used in the output. - -One consequence is that every trial result can have an impact on the search result. -That means if your SUT (or your traffic generator) needs a warmup, -be sure to warm it up before starting the search. - -## Exceed Ratio - -The idea of performing multiple trials at the same load comes from -a model where some trial results (those with high loss) are affected -by infrequent effects, causing poor repeatability of [RFC2544] throughput results. -See the discussion about noiseful and noiseless ends -of the SUT performance spectrum in section [DUT in SUT] (#DUT-in-SUT). -Stable results are closer to the noiseless end of the SUT performance spectrum, -so MLRsearch may need to allow some frequency of high-loss trials -to ignore the rare but big effects near the noiseful end. - -MLRsearch can do such trial result filtering, but it needs -a configuration option to tell it how frequent can the infrequent big loss be. -This option is called the exceed ratio. -It tells MLRsearch what ratio of trials -(more exactly what ratio of trial seconds) can have a [Trial Loss Ratio] (#Trial-Loss-Ratio) -larger than the Goal Loss Ratio and still be classified as a lower bound. -Zero exceed ratio means all trials have to have a Trial Loss Ratio -equal to or smaller than the Goal Loss Ratio. - -For explainability reasons, the RECOMMENDED value for exceed ratio is 0.5, -as it simplifies some later concepts by relating them to the concept of median. - -## Duration Sum - -When more than one trial is intended to classify a load, -MLRsearch also needs something that controls the number of trials needed. -Therefore, each goal also has an attribute called duration sum. - -The meaning of a [Goal Duration Sum] (#Goal-Duration-Sum) is that -when a load has (full-length) trials -whose trial durations when summed up give a value at least as big -as the Goal Duration Sum value, -the load is guaranteed to be classified either as an upper bound -or a lower bound for that goal. - -Due to the fact that the duration sum has a big impact -on the overall search duration, and [RFC2544] prescribes -wait intervals around trial traffic, -the MLRsearch algorithm is allowed to sum durations that are different -from the actual trial traffic durations. - -In the MLRsearch specification, the different duration values are called -[Trial Effective Duration] (#Trial-Effective-Duration). - -## Short Trials - -MLRsearch requires each goal to specify its final trial duration. -Full-length trial is a shorter name for a trial whose intended trial duration -is equal to (or longer than) the goal final trial duration. - -Section 24 of [RFC2544] already anticipates possible time savings -when short trials (shorter than full-length trials) are used. -Full-length trials are the opposite of short trials, -so they may also be called long trials. - -Any MLRsearch implementation may include its own configuration options -which control when and how MLRsearch chooses to use short trial durations. - -For explainability reasons, when exceed ratio of 0.5 is used, -it is recommended for the Goal Duration Sum to be an odd multiple -of the full trial durations, so Conditional Throughput becomes identical to -a median of a particular set of trial forwarding rates. - -The presence of short trial results complicates the load classification logic. - -Full details are given later in section [Logic of Load Classification] (#Logic-of-Load-Classification). -In a nutshell, results from short trials -may cause a load to be classified as an upper bound. -This may cause loss inversion, and thus lower the Relevant Lower Bound, -below what would classification say when considering full-length trials only. - -{::comment} - [I still think this is important, revisit after explanations re quantiles.] - - For explainability reasons, it is RECOMMENDED users use such configurations - that guarantee all trials have the same length. - - mk edit note: Using RFC2119 keyword here does not seem to be - appropriate. Moreover, I do not get the meaning nor the logic behind - this statement. It seems to say that in order for users to understand - the workings of MLRsearch, they should use simplified configuration, - otherwise they won't get it. Illogical it seems to me. Suggest to - remove it. - -{:/comment} - -{::comment} - [Important. Keeping compatibility slows search considerably.] - - Alas, such configurations are usually not compliant with [RFC2544] requirements, - or not time-saving enough. - - mk edit note: This statement does not make sense to me. Suggest to remove it. - -{:/comment} - -## Throughput - -{::comment} - [Important, we need better title.] - - [VP] TODO: Was named Conditional Troughput, but spec chapter already has one. - -{:/comment} - -Due to the fact that testing equipment takes the intended load as an input parameter -for a trial measurement, any load search algorithm needs to deal -with intended load values internally. - -But in the presence of goals with a non-zero loss ratio, the intended load -usually does not match the user's intuition of what a throughput is. -The forwarding rate (as defined in [RFC2285] section 3.6.1) is better, -but it is not obvious how to generalize it -for loads with multiple trial results and a non-zero -[Goal Loss Ratio] (#Goal-Loss-Ratio). - -The best example is also the main motivation: hard limit performance. -Even if the medium allows higher performance, -the SUT interfaces may have their additional own limitations, -e.g. a specific fps limit on the NIC (a very common occurance). - -Ideally, those should be known and used when computing Max Load. -But if Max Load is higher that what interface can receive or transmit, -there will be a "hard limit" observed in trial results. -Imagine the hard limit is at 100 Mfps, Max Load is higher, -and the goal loss ratio is 0.5%. If DUT has no additional losses, -0.5% loss ratio will be achieved at 100.5025 Mfps (the relevant lower bound). -But it is not intuitive to report SUT performance as a value that is -larger than known hard limit. -We need a generalization of RFC2544 throughput, -different from just the relevant lower bound. - -MLRsearch defines one such generalization, called the Conditional Throughput. -It is the trial forwarding rate from one of the trials -performed at the load in question. -Determining which trial exactly is defined in -[MLRsearch Specification] (#MLRsearch-Specification), -and in [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput). - -In the hard limit example, 100.5 Mfps load will still have -only 100.0 Mfps forwarding rate, nicely confirming the known limitation. - -Conditional Throughput is partially related to load classification. -If a load is classified as a lower bound for a goal, -the Conditional Throughput can be calculated from trial results, -and guaranteed to show an loss ratio -no larger than the Goal Loss Ratio. - -{::comment} - [Revisit after other edits, may be addressed elsewhere.] - - While the Conditional Throughput gives more intuitive-looking - values than the Relevant Lower Bound (for non-zero Goal Loss Ratio - values), the actual definition is more complicated than the definition - of the Relevant Lower Bound. - - mk edit note: Looking at this again, and per improved text, I - don't think it is that complicated. (BTW saying it is more complicated - and not addressing it, and leaving it open ended is not - good.) "Conditional throughput" intuitively is really throughput under - certain conditions, these being offered load determined by Relevant - Lower Bound and actual loss. For comparability, and taking into account - multiple trial samples, per MLRsearch definition, this is - mathematically expressed as `conditional_throughput = intended_load * - (1.0 - quantile_loss_ratio)`. - - DONE VP to MK: Hmm. Frequently, Conditional Throughput comes - from the worst among low-loss full-length trials. - But if two disparate goals are interested at the same load, - things get complicated (does not happen in CSIT production, - but I found few bugs when testing in simulator). - Computation in load classification is also not trivial, - but at least it only needs two "duration sum" values, - no need to sort all trial results. - - MKP2 [VP] TODO: Still not sure what to do with this subsection. - Possibly a bigger rewrite once VP and MK agree on what is (or is not) - complicated. :) - -{:/comment} - -{::comment} - [Important only for "design principles" chapter we may never have.] - - In the future, other intuitive values may become popular, - but they are unlikely to supersede the definition of the Relevant Lower Bound - as the most fitting value for comparability purposes, - therefore the Relevant Lower Bound remains a required attribute - of the Goal Result structure, while the Conditional Throughput is only optional. - - mk edit note: This paragraph adds to the confusion. I would remove - this paragraph, as with the new text above it doesn't seem to add any - value. - - [VP] TODO: This is an example of MLRsearch design principles. - -{:/comment} - -{::comment} - [Useful.] - - [VP] TODO: Mention somewhere that trending is a specific case - of repeatability/comparability. - -{:/comment} - -Note that when comparing the best (all zero loss) and worst case (all loss -just below Goal Loss Ratio), the same Relevant Lower Bound value -may result in the Conditional Throughput differing up to the Goal Loss Ratio. - -Therefore it is rarely needed to set the Goal Width (if expressed -as the relative difference of loads) below the Goal Loss Ratio. -In other words, setting the Goal Width below the Goal Loss Ratio -may cause the Conditional Throughput for a larger loss ratio to become smaller -than a Conditional Throughput for a goal with a smaller Goal Loss Ratio, -which is counter-intuitive, considering they come from the same search. -Therefore it is RECOMMENDED to set the Goal Width to a value no smaller -than the Goal Loss Ratio. - -Overall, this Conditional Throughput does behave well for comparability purposes. - -## Search Time - -MLRsearch was primarily developed to reduce the time -required to determine a throughput, either the [RFC2544] compliant one, -or some generalization thereof. -The art of achieving short search times -is mainly in the smart selection of intended loads (and intended durations) -for the next trial to perform. - -While there is an indirect impact of the load selection on the reported values, -in practice such impact tends to be small, -even for SUTs with quite a broad performance spectrum. - -A typical example of two approaches to load selection leading to different -Relevant Lower Bounds is when the interval is split in a very uneven way. -Any implementation choosing loads very close to the current Relevant Lower Bound -is quite likely to eventually stumble upon a trial result -with poor performance (due to SUT noise). -For an implementation choosing loads very close -to the current Relevant Upper Bound, this is unlikely, -as it examines more loads that can see a performance -close to the noiseless end of the SUT performance spectrum. - -However, as even splits optimize search duration at give precision, -MLRsearch implementations that prioritize minimizing search time -are unlikely to suffer from any such bias. - -Therefore, this document remains quite vague on load selection -and other optimization details, and configuration attributes related to them. -Assuming users prefer libraries that achieve short overall search time, -the definition of the Relevant Lower Bound -should be strict enough to ensure result repeatability -and comparability between different implementations, -while not restricting future implementations much. - -{::comment} - [Important for BMWG. Configurability is bad for comparability.] - - MKP2 Sadly, different implementations may exhibit their sweet spot of - the best repeatability for a given search duration - at different goals attribute values, especially concerning - any optional goal attributes such as the initial trial duration. - Thus, this document does not comment much on which configurations - are good for comparability between different implementations. - For comparability between different SUTs using the same implementation, - refer to configurations recommended by that particular implementation. - - MKP2 mk edit note: Isn't this going off on a tangent, hypothesising and - second guessing about different possible implementations. What is the - value of this content to this document? Suggest to remove it. - -{:/comment} - -## [RFC2544] Compliance - -Some Search Goal instances lead to results compliant with RFC2544. -See [RFC2544 Goal] (#RFC2544-Goal) for more details -regarding both conditional and unconditional compliance. - -The presence of other Search Goals does not affect the compliance -of this Goal Result. -The Relevant Lower Bound and the Conditional Throughput are in this case -equal to each other, and the value is the [RFC2544] throughput. - -# Logic of Load Classification - -## Introductory Remarks - -This chapter continues with explanations, -but this time more precise definitions are needed -for readers to follow the explanations. - -Descriptions in this section are wordy and implementers should read -[MLRsearch Specification] (#MLRsearch-Specification) section -and Appendices for more concise definitions. - -The two areas of focus here are load classification -and the Conditional Throughput. - -To start with [Performance Spectrum] (#Performance-Spectrum) -subsection contains definitions needed to gain insight -into what Conditional Throughput means. -Remaining subsections discuss load classification. - -For load classification, it is useful to define **good trials** and **bad trials**: - -- **Bad trial**: Trial is called bad (according to a goal) - if its [Trial Loss Ratio] (#Trial-Loss-Ratio) - is larger than the [Goal Loss Ratio] (#Goal-Loss-Ratio). - -- **Good trial**: Trial that is not bad is called good. - -## Performance Spectrum -### Description - -There are several equivalent ways to explain the Conditional Throughput -computation. One of the ways relies on performance -spectrum. - -Take an intended load value, a trial duration value, and a finite set -of trial results, with all trials measured at that load value and duration value. - -The performance spectrum is the function that maps -any non-negative real number into a sum of trial durations among all trials -in the set, that has that number, as their trial forwarding rate, -e.g. map to zero if no trial has that particular forwarding rate. - -A related function, defined if there is at least one trial in the set, -is the performance spectrum divided by the sum of the durations -of all trials in the set. - -That function is called the performance probability function, as it satisfies -all the requirements for probability mass function -of a discrete probability distribution, -the one-dimensional random variable being the trial forwarding rate. - -These functions are related to the SUT performance spectrum, -as sampled by the trials in the set. - -{::comment} - [Middle of rewrite?] - - MKP1 The performance spectrum is the function that maps - any non-negative real number into a sum of trial durations among all trials - in the set, that has that number, as their trial forwarding rate, - e.g. map to zero if no trial has that particular forwarding rate. - - MKP1 A related function, defined if there is at least one trial in the set, - is the performance spectrum divided by the sum of the durations - of all trials in the set. - - MKP1 That function is called the performance probability function, as it satisfies - all the requirements for probability mass function - of a discrete probability distribution, - the one-dimensional random variable being the trial forwarding rate. - - MKP1 These functions are related to the SUT performance spectrum, - as sampled by the trials in the set. - - MKP1 [VP] TODO: Introduce quantiles properly by incorporating the below. - - MKP1 [VP] TODO: "q-quantile" is plainly wrong. I meant the "p" in "p-quantile". - - - wikipedia: The 100-quantiles are called percentiles - - also wiki: If, instead of using integers k and q, the "p-quantile" is based on a real number p with 0 < p < 1 then... - - https://en.wikipedia.org/wiki/Quantile_function - - exceed ratio is an input to a quantile function: percentage? - - - MKP1 mk2 TODO for VP: Above is not making it clearer at all. Can't we really not explain the spectrum and exceed ratio with just percentiles and quantiles? - - As for any other probability function, we can talk about percentiles - of the performance probability function, including the median. - The Conditional Throughput will be one such quantile value - for a specifically chosen set of trials. - - MKP2 As for any other probability function, we can talk about percentiles - of the performance probability function, including the median. - The Conditional Throughput will be one such quantile value - for a specifically chosen set of trials. - -{:/comment} - -Take a set of all full-length trials performed at the Relevant Lower Bound, -sorted by decreasing trial forwarding rate. -The sum of the durations of those trials -may be less than the Goal Duration Sum, or not. -If it is less, add an imaginary trial result with zero trial forwarding rate, -such that the new sum of durations is equal to the Goal Duration Sum. -This is the set of trials to use. - -If the quantile touches two trials, - -{::comment} - [Clarity.] - - mk edit note: What does it mean "quantile touches two trials"? - Do you mean two trials are within specific quantile or percentile? - -{:/comment} - -the larger trial forwarding rate (from the trial result sorted earlier) is used. - -{::comment} - [Oh, unspecified exceed ratio?] - - the larger trial forwarding rate (from the trial result sorted earlier) is used. - - mk edit note: Why is that? Is it because you silently assumed that - quantile here is median or 50th percentile? - -{:/comment} - -The resulting quantity is the Conditional Throughput of the goal in question. - -{::comment} - [Motivation has lead to code. Now code is definition, this should be equivalent.] - - The resulting quantity is the Conditional Throughput of the goal in question. - - mk edit note: Is this is supposed to be another definition of - Conditional Throughput? If so, how does this relate to Performance - Spectrum? I suggest to either remove these unclear paragraphs above and - rely on examples below that are clear, or rework above so it fits the - flow. Cause right now it's confusion. Even more so, that - [Conditional Throughput] (#Conditional-Throughput) has been already - defined elsewhere in the document. - -{:/comment} - -A set of examples follows. - -### First Example - -- [Goal Exceed Ratio] (#Goal-Exceed-Ratio) = 0 and [Goal Duration Sum] (#Goal-Duration-Sum) has been reached. -- Conditional Throughput is the smallest trial forwarding rate among the trials. - -### Second Example - -- Goal Exceed Ratio = 0 and Goal Duration Sum has not been reached yet. -- Due to the missing duration sum, the worst case may still happen, so the Conditional Throughput is zero. -- This is not reported to the user, as this load cannot become the Relevant Lower Bound yet. - -### Third Example - -- Goal Exceed Ratio = 50% and Goal Duration Sum is two seconds. -- One trial is present with the duration of one second and zero loss. -- The imaginary trial is added with the duration of one second and zero trial forwarding rate. -- The median would touch both trials, so the Conditional Throughput is the trial forwarding rate of the one non-imaginary trial. -- As that had zero loss, the value is equal to the offered load. - -{::comment} - [Middle of rewrite?] - - MKP2 mk edit note: how is the median "touching" both trials? - Isn't median of even set of data samples - the average of the two middle data points, - in this case the non-imaginary trial and the imaginary one? - - MKP2 Note that Appendix B does not take into account short trial results. - - MKP2 mk edit note: Whis is this relevant here? Appendix B has not been mentioned in this section. - -{:/comment} - -### Summary - -While the Conditional Throughput is a generalization of the trial forwarding rate, -its definition is not an obvious one. - -Other than the trial forwarding rate, the other source of intuition -is the quantile in general, and the median the recommended case. - -{::comment} - [Next version of MLRsearch library may invent new quantity that is more stable.] - - In future, different quantities may prove more useful, - especially when applying to specific problems, - but currently the Conditional Throughput is the recommended compromise, - especially for repeatability and comparability reasons. - - MKP2 mk edit note: This is future looking and hand wavy without - specifics. What are the "specific problems" that are referred here? - Networking, else?Some specific behaviours, if so, what sort? If - something is classified as future work, it needs to be better defined. - The same applies to any out of scope statements. - -{:/comment} - -## Trials with Single Duration - -{::comment} - [Clarity.] - - MKP2 mk edit note: Need to improve explanations in this subsection. - -{:/comment} - -When goal attributes are chosen in such a way that every trial has the same -intended duration, the load classification is simpler. - -The following description follows the motivation -of Goal Loss Ratio, Goal Exceed Ratio, and Goal Duration Sum. - -If the sum of the durations of all trials (at the given load) -is less than the Goal Duration Sum, imagine two scenarios: - -- **best case scenario**: all subsequent trials having zero loss, and -- **worst case scenario**: all subsequent trials having 100% loss. - -Here we assume there are as many subsequent trials as needed -to make the sum of all trials equal to the Goal Duration Sum. - -The exceed ratio is defined using sums of durations -(and number of trials does not matter), so it does not matter whether -the "subsequent trials" can consist of an integer number of full-length trials. - -In any of the two scenarios, best case and worst case, we can compute the load exceed ratio, -as the duration sum of good trials divided by the duration sum of all trials, -in both cases including the assumed trials. - -Even if, in the best case scenario, the load exceed ratio is larger -than the Goal Exceed Ratio, the load is an upper bound. - -MKP2 Even if, in the worst case scenario, the load exceed ratio is not larger -than the Goal Exceed Ratio, the load is a lower bound. - -{::comment} - [Middle of rewrite?] - - Even if, in the best case scenario, the load exceed ratio is larger - than the Goal Exceed Ratio, the load is an upper bound. - - MKP2 Even if, in the worst case scenario, the load exceed ratio is not larger - than the Goal Exceed Ratio, the load is a lower bound. - - MKP2 mk edit note: I am confused by "Even if" prefixing - each of the above statements. And even more so by your version - with "If even". - - mk edit note: I do not get how this statements are true, as they - are counter-intuitive. For the best case scenario, if load exceed ratio - is larger than the goal exceed ratio, I expect the load to be lower - bound. Need more examples. - -{:/comment} - -More specifically: - -- Take all trials measured at a given load. -- The sum of the durations of all bad full-length trials is called the bad sum. -- The sum of the durations of all good full-length trials is called the good sum. -- The result of adding the bad sum plus the good sum is called the measured sum. -- The larger of the measured sum and the Goal Duration Sum is called the whole sum. -- The whole sum minus the measured sum is called the missing sum. -- The optimistic exceed ratio is the bad sum divided by the whole sum. -- The pessimistic exceed ratio is the bad sum plus the missing sum, that divided by the whole sum. -- If the optimistic exceed ratio is larger than the Goal Exceed Ratio, the load is classified as an upper bound. -- If the pessimistic exceed ratio is not larger than the Goal Exceed Ratio, the load is classified as a lower bound. -- Else, the load is classified as undecided. - -The definition of pessimistic exceed ratio is compatible with the logic in -the Conditional Throughput computation, so in this single trial duration case, -a load is a lower bound if and only if the Conditional Throughput -loss ratio is not larger than the Goal Loss Ratio. - -{::comment} - [Useful (depends on the whole chapter).] - - MKP2 mk edit note: I do not get the defintion of optimistic and - pessmistic exceed ratios. Please define or describe what they - are. - -{:/comment} - -If it is larger, the load is either an upper bound or undecided. - -## Trials with Short Duration - -### Scenarios - -Trials with intended duration smaller than the goal final trial duration -are called short trials. -The motivation for load classification logic in the presence of short trials -is based around a counter-factual case: What would the trial result be -if a short trial has been measured as a full-length trial instead? - -There are three main scenarios where human intuition guides -the intended behavior of load classification. - -#### False Good Scenario - -The user had their reason for not configuring a shorter goal -final trial duration. -Perhaps SUT has buffers that may get full at longer -trial durations. -Perhaps SUT shows periodic decreases in performance -the user does not want to be treated as noise. - -In any case, many good short trials may become bad full-length trials -in the counter-factual case. - -In extreme cases, there are plenty of good short trials and no bad short trials. - -In this scenario, we want the load classification NOT to classify the load -as a lower bound, despite the abundance of good short trials. - -{::comment} - [I agree.] - - MKP2 mk edit note: It may be worth adding why that is. i.e. because - there is a risk that at longer trial this could turn into a bad - trial. - -{:/comment} - -Effectively, we want the good short trials to be ignored, so they -do not contribute to comparisons with the Goal Duration Sum. - -#### True Bad Scenario - -When there is a frame loss in a short trial, -the counter-factual full-length trial is expected to lose at least as many -frames. - -In practice, bad short trials are rarely turning into -good full-length trials. - -In extreme cases, there are no good short trials. - -In this scenario, we want the load classification -to classify the load as an upper bound just based on the abundance -of short bad trials. - -Effectively, we want the bad short trials -to contribute to comparisons with the Goal Duration Sum, -so the load can be classified sooner. - -#### Balanced Scenario - -Some SUTs are quite indifferent to trial duration. -Performance probability function constructed from short trial results -is likely to be similar to the performance probability function constructed -from full-length trial results (perhaps with larger dispersion, -but without a big impact on the median quantiles overall). - -{::comment} - [Recheck after edits earlier.] - - MKP1 mk edit note: "Performance probability function" is this function - defined anywhere? Mention in [Performance Spectrum] (#Performance Spectrum) - is not a complete definition. - -{:/comment} - -For a moderate Goal Exceed Ratio value, this may mean there are both -good short trials and bad short trials. - -This scenario is there just to invalidate a simple heuristic -of always ignoring good short trials and never ignoring bad short trials, -as that simple heuristic would be too biased. - -Yes, the short bad trials -are likely to turn into full-length bad trials in the counter-factual case, -but there is no information on what would the good short trials turn into. - -The only way to decide safely is to do more trials at full length, -the same as in False Good Scenario. - -### Classification Logic - -MLRsearch picks a particular logic for load classification -in the presence of short trials, but it is still RECOMMENDED -to use configurations that imply no short trials, -so the possible inefficiencies in that logic -do not affect the result, and the result has better explainability. - -With that said, the logic differs from the single trial duration case -only in different definition of the bad sum. -The good sum is still the sum across all good full-length trials. - -Few more notions are needed for defining the new bad sum: - -- The sum of durations of all bad full-length trials is called the bad long sum. -- The sum of durations of all bad short trials is called the bad short sum. -- The sum of durations of all good short trials is called the good short sum. -- One minus the Goal Exceed Ratio is called the subceed ratio. -- The Goal Exceed Ratio divided by the subceed ratio is called the exceed coefficient. -- The good short sum multiplied by the exceed coefficient is called the balancing sum. -- The bad short sum minus the balancing sum is called the excess sum. -- If the excess sum is negative, the bad sum is equal to the bad long sum. -- Otherwise, the bad sum is equal to the bad long sum plus the excess sum. - -Here is how the new definition of the bad sum fares in the three scenarios, -where the load is close to what would the relevant bounds be -if only full-length trials were used for the search. - -#### False Good Scenario - -If the duration is too short, we expect to see a higher frequency -of good short trials. -This could lead to a negative excess sum, -which has no impact, hence the load classification is given just by -full-length trials. -Thus, MLRsearch using too short trials has no detrimental effect -on result comparability in this scenario. -But also using short trials does not help with overall search duration, -probably making it worse. - -#### True Bad Scenario - -Settings with a small exceed ratio -have a small exceed coefficient, so the impact of the good short sum is small, -and the bad short sum is almost wholly converted into excess sum, -thus bad short trials have almost as big an impact as full-length bad trials. -The same conclusion applies to moderate exceed ratio values -when the good short sum is small. -Thus, short trials can cause a load to get classified as an upper bound earlier, -bringing time savings (while not affecting comparability). - -#### Balanced Scenario - -Here excess sum is small in absolute value, as the balancing sum -is expected to be similar to the bad short sum. -Once again, full-length trials are needed for final load classification; -but usage of short trials probably means MLRsearch needed -a shorter overall search time before selecting this load for measurement, -thus bringing time savings (while not affecting comparability). - -Note that in presence of short trial results, -the comparibility between the load classification -and the Conditional Throughput is only partial. -The Conditional Throughput still comes from a good long trial, -but a load higher than the Relevant Lower Bound may also compute to a good value. - -## Trials with Longer Duration - -If there are trial results with an intended duration larger -than the goal trial duration, the precise definitions -in Appendix A and Appendix B treat them in exactly the same way -as trials with duration equal to the goal trial duration. - -But in configurations with moderate (including 0.5) or small -Goal Exceed Ratio and small Goal Loss Ratio (especially zero), -bad trials with longer than goal durations may bias the search -towards the lower load values, as the noiseful end of the spectrum -gets a larger probability of causing the loss within the longer trials. - -{::comment} - [Use single goal when testing externaly, deviate freely in internal tests.] - - For some users, this is an acceptable price - for increased configuration flexibility - (perhaps saving time for the related goals), - so implementations SHOULD allow such configurations. - Still, users are encouraged to avoid such configurations - by making all goals use the same final trial duration, - so their results remain comparable across implementations. - - MKP2 mk edit note: This paragraph has no value in my view. - Statements like "For some users, this is an acceptable price - for increased configuration flexibility" do not make sense. - Configuration flexibility for flexibility sake is not a valid argument - in the specification that aims at standardising benchmarking methodologies. - If one wants to test with longer durations, - then one should configure these as Goal Final Trial Duration. - Simple, no? Or am I reading this point wrong? - -{:/comment} - -{::comment} - [MKP4 Out of scope here, subject for future work] - - # Current practices? - - MKP2 [VP] TODO: Even if not mentioned in spec (not even recommended), - some tricks from CSIT code may be worth mentioning? Not sure. - - MKP2 [VP] TODO: Tricks with big impact on search time - can be mentioned so that Addressed Problems : Long Test Duration - has something specific to refer to. - - MKP2 [VP] TODO: It is important to mention trick that have impact - on repeatability and comparability. - - MKP2 [VP] TODO: CSIT computes a discrete "grid" of load values to use. - - MKP2 [VP] TODO: - If all Goal Widths are aligned, there is one common coarse grid. - In that case, NDR (and even PDR conditional throughput - for tests with zer-or-big losses) values are identical in trending, - hiding the real performance variance, and causing fake anomaly - when the performance shifts just one gridpoint. - - - MKP2 [VP] TODO: Conversely, when Goal Width do not match well, - CSIT needs to compute a fine-grained grid to match them all. - In this case, similar performances can be "rounded differently", - mostly based on specific loss that happened at Max Load, - where SUT may be less stable than around PDR. - This way trending sees higher variance (still within corresponding Goal Width), - but at least there are no fake anomalies. - - - MKP2 [VP] TODO: In general, do not trust stdev if not larged than width. - - MKP2 [VP] TODO: De we have a chapter section fosucing on design principles? - - Make Controller API independent from Measurer API. - - The "allowed if makes worse" principle: - - RFC1242 specmanship happens when testing own DUTs. - - Shortening trial wait times only risks making goal results lower. - - So it is fine to save time aggressively when testing own DUTs. - - -{:/comment} - - -{::comment} - [Will be nice if made substantial.] - - # Addressed Problems - - MKP1 all of this section requires updating based on the updated content. - And it is for information only anyways. In fact not sure it's needed. - Maybe in appendix for posterity. - - Now when MLRsearch is clearly specified and explained, - it is possible to summarize how does MLRsearch specification help with problems. - - Here, "multiple trials" is a shorthand for having the goal final trial duration - significantly smaller than the Goal Duration Sum. - This results in MLRsearch performing multiple trials at the same load, - which may not be the case with other configurations. - - ## Long Test Duration - - As shortening the overall search duration is the main motivation - of MLRsearch library development, the library implements - multiple improvements on this front, both big and small. - - Most of implementation details are not constrained by MLRsearch specification, - so that future implementations may keep shortening the search duration even more. - - One exception is the impact of short trial results on the Relevant Lower Bound. - While motivated by human intuition, the logic is not straightforward. - In practice, configurations with only one common trial duration value - are capable of achieving good overal search time and result repeatability - without the need to consider short trials. - - ### Impact of goal attribute values - - From the required goal attributes, the Goal Duration Sum - remains the best way to get even shorter searches. - - Usage of multiple trials can also save time, - depending on wait times around trial traffic. - - The farther the Goal Exceed Ratio is from 0.5 (towards zero or one), - the less predictable the overal search duration becomes in practice. - - Width parameter does not change search duration much in practice - (compared to other, mainly optional goal attributes). - - ## DUT in SUT - - In practice, using multiple trials and moderate exceed ratios - often improves result repeatability without increasing the overall search time, - depending on the specific SUT and DUT characteristics. - Benefits for separating SUT noise are less clear though, - as it is not easy to distinguish SUT noise from DUT instability in general. - - Conditional Throughput has an intuitive meaning when described - using the performance spectrum, so this is an improvement - over existing simple (less configurable) search procedures. - - Multiple trials can save time also when the noisy end of - the preformance spectrum needs to be examined, e.g. for [RFC9004]. - - Under some circumstances, testing the same DUT and SUT setup with different - DUT configurations can give some hints on what part of noise is SUT noise - and what part is DUT performance fluctuations. - In practice, both types of noise tend to be too complicated for that analysis. - - MLRsearch enables users to search for multiple goals, - potentially providing more insight at the cost of a longer overall search time. - However, for a thorough and reliable examination of DUT-SUT interactions, - it is necessary to employ additional methods beyond black-box benchmarking, - such as collecting and analyzing DUT and SUT telemetry. - - ## Repeatability and Comparability - - Multiple trials improve repeatability, depending on exceed ratio. - - In practice, one-second goal final trial duration with exceed ratio 0.5 - is good enough for modern SUTs. - However, unless smaller wait times around the traffic part of the trial - are allowed, too much of overal search time would be wasted on waiting. - - It is not clear whether exceed ratios higher than 0.5 are better - for repeatability. - The 0.5 value is still preferred due to explainability using median. - - It is possible that the Conditional Throughput values (with non-zero goal - loss ratio) are better for repeatability than the Relevant Lower Bound values. - This is especially for implementations - which pick load from a small set of discrete values, - as that hides small variances in Relevant Lower Bound values - other implementations may find. - - Implementations focusing on shortening the overall search time - are automatically forced to avoid comparability issues due to load selection, - as they must prefer even splits wherever possible. - But this conclusion only holds when the same goals are used. - Larger adoption is needed before any further claims on comparability - between MLRsearch implementations can be made. - - ## Throughput with Non-Zero Loss - - Trivially suported by the Goal Loss Ratio attribute. - - In practice, usage of non-zero loss ratio values - improves the result repeatability - (exactly as expected based on results from simpler search methods). - - ## Inconsistent Trial Results - - MLRsearch is conservative wherever possible. - This is built into the definition of Conditional Throughput, - and into the treatment of short trial results for load classification. - - This is consistent with [RFC2544] zero loss tolerance motivation. - - If the noiseless part of the SUT performance spectrum is of interest, - it should be enough to set small value for the goal final trial duration, - and perhaps also a large value for the Goal Exceed Ratio. - - Implementations may offer other (optional) configuration attributes - to become less conservative, but currently it is not clear - what impact would that have on repeatability. - -{:/comment} - -# IANA Considerations - -No requests of IANA. - -# Security Considerations - -Benchmarking activities as described in this memo are limited to -technology characterization of a DUT/SUT using controlled stimuli in a -laboratory environment, with dedicated address space and the constraints -specified in the sections above. - -The benchmarking network topology will be an independent test setup and -MUST NOT be connected to devices that may forward the test traffic into -a production network or misroute traffic to the test management network. - -Further, benchmarking is performed on a "black-box" basis, relying -solely on measurements observable external to the DUT/SUT. - -Special capabilities SHOULD NOT exist in the DUT/SUT specifically for -benchmarking purposes. Any implications for network security arising -from the DUT/SUT SHOULD be identical in the lab and in production -networks. - -# Acknowledgements - -Some phrases and statements in this document were created -with help of Mistral AI (mistral.ai). - -Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough -review and numerous useful comments and suggestions in the earlier versions of this document. - -Special wholehearted gratitude and thanks to the late Al Morton for his -thorough reviews filled with very specific feedback and constructive -guidelines. Thank you Al for the close collaboration over the years, -for your continuous unwavering encouragement full of empathy and -positive attitude. Al, you are dearly missed. - -# Appendix A: Load Classification - -This section specifies how to perform the load classification. - -Any intended load value can be classified, according to a given [Search Goal] (#Search-Goal). - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the Measurer. - -The block at the end of this appendix holds pseudocode -which computes two values, stored in variables named -`optimistic` and `pessimistic`. - -{::comment} - [We have other section re optimistic. Not going to talk about variable naming here.] - - MKP2 mk edit note: Need to add the description of what - the `optimistic` and `pessimistic` variables represent. - Or a reference to where this is described - e.g. in [Single Trial Duration] (#Single-Trial-Duration) section. - -{:/comment} - -The pseudocode happens to be a valid Python code. - -If values of both variables are computed to be true, the load in question -is classified as a lower bound according to the given Search Goal. -If values of both variables are false, the load is classified as an upper bound. -Otherwise, the load is classified as undecided. - -The pseudocode expects the following variables to hold values as follows: - -- `goal_duration_sum`: The duration sum value of the given Search Goal. - -- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. - -- `good_long_sum`: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial Loss Ratio - not higher than the Goal Loss Ratio. - -- `bad_long_sum`: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial Loss Ratio - higher than the Goal Loss Ratio. - -- `good_short_sum`: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a Trial Loss Ratio - not higher than the Goal Loss Ratio. - -- `bad_short_sum`: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a Trial Loss Ratio - higher than the Goal Loss Ratio. - -The code works correctly also when there are no trial results at a given load. - -~~~ python -balancing_sum = good_short_sum * goal_exceed_ratio / (1.0 - goal_exceed_ratio) -effective_bad_sum = bad_long_sum + max(0.0, bad_short_sum - balancing_sum) -effective_whole_sum = max(good_long_sum + effective_bad_sum, goal_duration_sum) -quantile_duration_sum = effective_whole_sum * goal_exceed_ratio -optimistic = effective_bad_sum <= quantile_duration_sum -pessimistic = (effective_whole_sum - good_long_sum) <= quantile_duration_sum -~~~ - -# Appendix B: Conditional Throughput - -This section specifies how to compute Conditional Throughput, as referred to in section [Conditional Throughput] (#Conditional-Throughput). - -Any intended load value can be used as the basis for the following computation, -but only the Relevant Lower Bound (at the end of the search) -leads to the value called the Conditional Throughput for a given Search Goal. - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the Measurer. - -The block at the end of this appendix holds pseudocode -which computes a value stored as variable `conditional_throughput`. - -{::comment} - [CT is CT. But text could make more obvious.] - - MKP2 mk edit note: Need to add the description of what does - the `conditional_throughput` variable represent. - Or a reference to where this is described - e.g. in [Conditional Throughput] (#Conditional-Throughput) section. - -{:/comment} - -The pseudocode happens to be a valid Python code. - -The pseudocode expects the following variables to hold values as follows: - -- `goal_duration_sum`: The duration sum value of the given Search Goal. - -- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. - -- `good_long_sum`: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial Loss Ratio - not higher than the Goal Loss Ratio. - -- `bad_long_sum`: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial Loss Ratio - higher than the Goal Loss Ratio. - -- `long_trials`: An iterable of all trial results from trials with trial duration - at least equal to the goal final trial duration, - sorted by increasing the Trial Loss Ratio. - A trial result is a composite with the following two attributes available: - - - `trial.loss_ratio`: The Trial Loss Ratio as measured for this trial. - - - `trial.duration`: The trial duration of this trial. - -The code works correctly only when there if there is at least one -trial result measured at a given load. - -~~~ python -all_long_sum = max(goal_duration_sum, good_long_sum + bad_long_sum) -remaining = all_long_sum * (1.0 - goal_exceed_ratio) -quantile_loss_ratio = None -for trial in long_trials: - if quantile_loss_ratio is None or remaining > 0.0: - quantile_loss_ratio = trial.loss_ratio - remaining -= trial.duration - else: - break -else: - if remaining > 0.0: - quantile_loss_ratio = 1.0 -conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) -~~~ - ---- back - -{::comment} - [Final checklist.] - - [VP] Final Checks. Only mark as done when there are no active todos above. - - [VP] Rename chapter/sub-/section to better match their content. - - MKP3 [VP] TODO: Recheck the definition dependencies go bottom-up. - - [VP] TODO: Unify external reference style (brackets, spaces, section numbers and names). - - [VP] TODO: Add internal links wherever Captialized Term is mentioned. - - MKP2 [VP] TODO: Capitalization of New Terms: useful when editing and reviewing, - but I still vote to remove capitalization before final submit, - because all other RFCs I see only capitalize due to being section title. - - [VP] TODO: If time permits, keep improving formal style (e.g. using AI). - -{:/comment} diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.txt b/docs/ietf/draft-ietf-bmwg-mlrsearch-07.txt deleted file mode 100644 index c5c94410a8..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.txt +++ /dev/null @@ -1,2800 +0,0 @@ - - - - -Benchmarking Working Group M. Konstantynowicz -Internet-Draft V. Polak -Intended status: Informational Cisco Systems -Expires: 18 January 2025 18 July 2024 - - - Multiple Loss Ratio Search - draft-ietf-bmwg-mlrsearch-07 - -Abstract - - This document proposes extensions to [RFC2544] throughput search by - defining a new methodology called Multiple Loss Ratio search - (MLRsearch). MLRsearch aims to minimize search duration, support - multiple loss ratio searches, and enhance result repeatability and - comparability. - - The primary reason for extending [RFC2544] is to address the - challenges and requirements presented by the evaluation and testing - of software-based networking systems' data planes. - - To give users more freedom, MLRsearch provides additional - configuration options such as allowing multiple short trials per load - instead of one large trial, tolerating a certain percentage of trial - results with higher loss, and supporting the search for multiple - goals with varying loss ratios. - -Status of This Memo - - This Internet-Draft is submitted in full conformance with the - provisions of BCP 78 and BCP 79. - - Internet-Drafts are working documents of the Internet Engineering - Task Force (IETF). Note that other groups may also distribute - working documents as Internet-Drafts. The list of current Internet- - Drafts is at https://datatracker.ietf.org/drafts/current/. - - Internet-Drafts are draft documents valid for a maximum of six months - and may be updated, replaced, or obsoleted by other documents at any - time. It is inappropriate to use Internet-Drafts as reference - material or to cite them other than as "work in progress." - - This Internet-Draft will expire on 18 January 2025. - -Copyright Notice - - Copyright (c) 2024 IETF Trust and the persons identified as the - document authors. All rights reserved. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 1] - -Internet-Draft MLRsearch July 2024 - - - This document is subject to BCP 78 and the IETF Trust's Legal - Provisions Relating to IETF Documents (https://trustee.ietf.org/ - license-info) in effect on the date of publication of this document. - Please review these documents carefully, as they describe your rights - and restrictions with respect to this document. Code Components - extracted from this document must include Revised BSD License text as - described in Section 4.e of the Trust Legal Provisions and are - provided without warranty as described in the Revised BSD License. - -Table of Contents - - 1. Purpose and Scope . . . . . . . . . . . . . . . . . . . . . . 4 - 2. Identified Problems . . . . . . . . . . . . . . . . . . . . . 5 - 2.1. Long Search Duration . . . . . . . . . . . . . . . . . . 5 - 2.2. DUT in SUT . . . . . . . . . . . . . . . . . . . . . . . 6 - 2.3. Repeatability and Comparability . . . . . . . . . . . . . 8 - 2.4. Throughput with Non-Zero Loss . . . . . . . . . . . . . . 8 - 2.5. Inconsistent Trial Results . . . . . . . . . . . . . . . 9 - 3. MLRsearch Specification . . . . . . . . . . . . . . . . . . . 10 - 3.1. Overview . . . . . . . . . . . . . . . . . . . . . . . . 10 - 3.2. Measurement Quantities . . . . . . . . . . . . . . . . . 11 - 3.3. Existing Terms . . . . . . . . . . . . . . . . . . . . . 12 - 3.3.1. SUT . . . . . . . . . . . . . . . . . . . . . . . . . 12 - 3.3.2. DUT . . . . . . . . . . . . . . . . . . . . . . . . . 12 - 3.3.3. Trial . . . . . . . . . . . . . . . . . . . . . . . . 12 - 3.4. Trial Terms . . . . . . . . . . . . . . . . . . . . . . . 13 - 3.4.1. Trial Duration . . . . . . . . . . . . . . . . . . . 14 - 3.4.2. Trial Load . . . . . . . . . . . . . . . . . . . . . 14 - 3.4.3. Trial Input . . . . . . . . . . . . . . . . . . . . . 15 - 3.4.4. Traffic Profile . . . . . . . . . . . . . . . . . . . 15 - 3.4.5. Trial Forwarding Ratio . . . . . . . . . . . . . . . 16 - 3.4.6. Trial Loss Ratio . . . . . . . . . . . . . . . . . . 16 - 3.4.7. Trial Forwarding Rate . . . . . . . . . . . . . . . . 17 - 3.4.8. Trial Effective Duration . . . . . . . . . . . . . . 17 - 3.4.9. Trial Output . . . . . . . . . . . . . . . . . . . . 18 - 3.4.10. Trial Result . . . . . . . . . . . . . . . . . . . . 18 - 3.5. Goal Terms . . . . . . . . . . . . . . . . . . . . . . . 19 - 3.5.1. Goal Final Trial Duration . . . . . . . . . . . . . . 19 - 3.5.2. Goal Duration Sum . . . . . . . . . . . . . . . . . . 19 - 3.5.3. Goal Loss Ratio . . . . . . . . . . . . . . . . . . . 20 - 3.5.4. Goal Exceed Ratio . . . . . . . . . . . . . . . . . . 20 - 3.5.5. Goal Width . . . . . . . . . . . . . . . . . . . . . 21 - 3.5.6. Search Goal . . . . . . . . . . . . . . . . . . . . . 21 - 3.5.7. Controller Input . . . . . . . . . . . . . . . . . . 22 - 3.6. Search Goal Examples . . . . . . . . . . . . . . . . . . 23 - 3.6.1. RFC2544 Goal . . . . . . . . . . . . . . . . . . . . 23 - 3.6.2. TST009 Goal . . . . . . . . . . . . . . . . . . . . . 24 - 3.7. Result Terms . . . . . . . . . . . . . . . . . . . . . . 24 - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 2] - -Internet-Draft MLRsearch July 2024 - - - 3.7.1. Relevant Upper Bound . . . . . . . . . . . . . . . . 25 - 3.7.2. Relevant Lower Bound . . . . . . . . . . . . . . . . 25 - 3.7.3. Conditional Throughput . . . . . . . . . . . . . . . 26 - 3.7.4. Goal Result . . . . . . . . . . . . . . . . . . . . . 26 - 3.7.5. Search Result . . . . . . . . . . . . . . . . . . . . 27 - 3.7.6. Controller Output . . . . . . . . . . . . . . . . . . 27 - 3.8. MLRsearch Architecture . . . . . . . . . . . . . . . . . 28 - 3.8.1. Measurer . . . . . . . . . . . . . . . . . . . . . . 28 - 3.8.2. Controller . . . . . . . . . . . . . . . . . . . . . 29 - 3.8.3. Manager . . . . . . . . . . . . . . . . . . . . . . . 29 - 3.9. Implementation Compliance . . . . . . . . . . . . . . . . 30 - 4. Additional Considerations . . . . . . . . . . . . . . . . . . 30 - 4.1. MLRsearch Versions . . . . . . . . . . . . . . . . . . . 31 - 4.2. Stopping Conditions . . . . . . . . . . . . . . . . . . . 31 - 4.3. Load Classification . . . . . . . . . . . . . . . . . . . 32 - 4.4. Loss Ratios . . . . . . . . . . . . . . . . . . . . . . . 32 - 4.5. Loss Inversion . . . . . . . . . . . . . . . . . . . . . 33 - 4.6. Exceed Ratio . . . . . . . . . . . . . . . . . . . . . . 34 - 4.7. Duration Sum . . . . . . . . . . . . . . . . . . . . . . 34 - 4.8. Short Trials . . . . . . . . . . . . . . . . . . . . . . 35 - 4.9. Throughput . . . . . . . . . . . . . . . . . . . . . . . 35 - 4.10. Search Time . . . . . . . . . . . . . . . . . . . . . . . 37 - 4.11. RFC2544 Compliance . . . . . . . . . . . . . . . . . . . 38 - 5. Logic of Load Classification . . . . . . . . . . . . . . . . 38 - 5.1. Introductory Remarks . . . . . . . . . . . . . . . . . . 38 - 5.2. Performance Spectrum . . . . . . . . . . . . . . . . . . 38 - 5.2.1. First Example . . . . . . . . . . . . . . . . . . . . 39 - 5.2.2. Second Example . . . . . . . . . . . . . . . . . . . 40 - 5.2.3. Third Example . . . . . . . . . . . . . . . . . . . . 40 - 5.2.4. Summary . . . . . . . . . . . . . . . . . . . . . . . 40 - 5.3. Trials with Single Duration . . . . . . . . . . . . . . . 40 - 5.4. Trials with Short Duration . . . . . . . . . . . . . . . 42 - 5.4.1. Scenarios . . . . . . . . . . . . . . . . . . . . . . 42 - 5.4.2. Classification Logic . . . . . . . . . . . . . . . . 43 - 5.5. Trials with Longer Duration . . . . . . . . . . . . . . . 45 - 6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 45 - 7. Security Considerations . . . . . . . . . . . . . . . . . . . 45 - 8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 46 - 9. Appendix A: Load Classification . . . . . . . . . . . . . . . 46 - 10. Appendix B: Conditional Throughput . . . . . . . . . . . . . 47 - 11. References . . . . . . . . . . . . . . . . . . . . . . . . . 49 - 11.1. Normative References . . . . . . . . . . . . . . . . . . 49 - 11.2. Informative References . . . . . . . . . . . . . . . . . 49 - Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 49 - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 3] - -Internet-Draft MLRsearch July 2024 - - -1. Purpose and Scope - - The purpose of this document is to describe Multiple Loss Ratio - search (MLRsearch), a data plane throughput search methodology - optimized for software networking DUTs. - - Applying vanilla [RFC2544] throughput bisection to software DUTs - results in several problems: - - * Binary search takes too long as most trials are done far from the - eventually found throughput. - - * The required final trial duration and pauses between trials - prolong the overall search duration. - - * Software DUTs show noisy trial results, leading to a big spread of - possible discovered throughput values. - - * Throughput requires a loss of exactly zero frames, but the - industry frequently allows for small but non-zero losses. - - * The definition of throughput is not clear when trial results are - inconsistent. - - To address the problems mentioned above, the MLRsearch test - methodology specification employs the following enhancements: - - * Allow multiple short trials instead of one big trial per load. - - - Optionally, tolerate a percentage of trial results with higher - loss. - - * Allow searching for multiple Search Goals, with differing loss - ratios. - - - Any trial result can affect each Search Goal in principle. - - * Insert multiple coarse targets for each Search Goal, earlier ones - need to spend less time on trials. - - - Earlier targets also aim for lesser precision. - - - Use Forwarding Rate (FR) at maximum offered load [RFC2285] - (section 3.6.2) to initialize the initial targets. - - * Take care when dealing with inconsistent trial results. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 4] - -Internet-Draft MLRsearch July 2024 - - - - Reported throughput is smaller than the smallest load with high - loss. - - - Smaller load candidates are measured first. - - * Apply several load selection heuristics to save even more time by - trying hard to avoid unnecessarily narrow bounds. - - Some of these enhancements are formalized as MLRsearch specification, - the remaining enhancements are treated as implementation details, - thus achieving high comparability without limiting future - improvements. - - MLRsearch configuration options are flexible enough to support both - conservative settings and aggressive settings. The conservative - settings lead to results unconditionally compliant with [RFC2544], - but longer search duration and worse repeatability. Conversely, - aggressive settings lead to shorter search duration and better - repeatability, but the results are not compliant with [RFC2544]. - - No part of [RFC2544] is intended to be obsoleted by this document. - -2. Identified Problems - - This chapter describes the problems affecting usability of various - performance testing methodologies, mainly a binary search for - [RFC2544] unconditionally compliant throughput. - -2.1. Long Search Duration - - The emergence of software DUTs, with frequent software updates and a - number of different frame processing modes and configurations, has - increased both the number of performance tests required to verify the - DUT update and the frequency of running those tests. This makes the - overall test execution time even more important than before. - - The current [RFC2544] throughput definition restricts the potential - for time-efficiency improvements. A more generalized throughput - concept could enable further enhancements while maintaining the - precision of simpler methods. - - The bisection method, when unconditionally compliant with [RFC2544], - is excessively slow. This is because a significant amount of time is - spent on trials with loads that, in retrospect, are far from the - final determined throughput. - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 5] - -Internet-Draft MLRsearch July 2024 - - - [RFC2544] does not specify any stopping condition for throughput - search, so users already have an access to a limited trade-off - between search duration and achieved precision. However, each full - 60-second trials doubles the precision, so not many trials can be - removed without a substantial loss of precision. - -2.2. DUT in SUT - - [RFC2285] defines: - DUT as - The network forwarding device to which - stimulus is offered and response measured [RFC2285] (section 3.1.1). - - SUT as - The collective set of network devices to which stimulus is - offered as a single entity and response measured [RFC2285] (section - 3.1.2). - - [RFC2544] specifies a test setup with an external tester stimulating - the networking system, treating it either as a single DUT, or as a - system of devices, an SUT. - - In the case of software networking, the SUT consists of not only the - DUT as a software program processing frames, but also of server - hardware and operating system functions, with that server hardware - resources shared across all programs including the operating system. - - Given that the SUT is a shared multi-tenant environment encompassing - the DUT and other components, the DUT might inadvertently experience - interference from the operating system or other software operating on - the same server. - - Some of this interference can be mitigated. For instance, pinning - DUT program threads to specific CPU cores and isolating those cores - can prevent context switching. - - Despite taking all feasible precautions, some adverse effects may - still impact the DUT's network performance. In this document, these - effects are collectively referred to as SUT noise, even if the - effects are not as unpredictable as what other engineering - disciplines call noise. - - DUT can also exhibit fluctuating performance itself, for reasons not - related to the rest of SUT. For example due to pauses in execution - as needed for internal stateful processing. In many cases this may - be an expected per-design behavior, as it would be observable even in - a hypothetical scenario where all sources of SUT noise are - eliminated. Such behavior affects trial results in a way similar to - SUT noise. As the two phenomenons are hard to distinguish, in this - document the term 'noise' is used to encompass both the internal - performance fluctuations of the DUT and the genuine noise of the SUT. - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 6] - -Internet-Draft MLRsearch July 2024 - - - A simple model of SUT performance consists of an idealized noiseless - performance, and additional noise effects. For a specific SUT, the - noiseless performance is assumed to be constant, with all observed - performance variations being attributed to noise. The impact of the - noise can vary in time, sometimes wildly, even within a single trial. - The noise can sometimes be negligible, but frequently it lowers the - observed SUT performance as observed in trial results. - - In this model, SUT does not have a single performance value, it has a - spectrum. One end of the spectrum is the idealized noiseless - performance value, the other end can be called a noiseful - performance. In practice, trial result close to the noiseful end of - the spectrum happens only rarely. The worse the performance value - is, the more rarely it is seen in a trial. Therefore, the extreme - noiseful end of the SUT spectrum is not observable among trial - results. Also, the extreme noiseless end of the SUT spectrum is - unlikely to be observable, this time because some small noise effects - are likely to occur multiple times during a trial. - - Unless specified otherwise, this document's focus is on the - potentially observable ends of the SUT performance spectrum, as - opposed to the extreme ones. - - When focusing on the DUT, the benchmarking effort should ideally aim - to eliminate only the SUT noise from SUT measurements. However, this - is currently not feasible in practice, as there are no realistic - enough models available to distinguish SUT noise from DUT - fluctuations, based on authors' experience and available literature. - - Assuming a well-constructed SUT, the DUT is likely its primary - performance bottleneck. In this case, we can define the DUT's ideal - noiseless performance as the noiseless end of the SUT performance - spectrum, especially for throughput. However, other performance - metrics, such as latency, may require additional considerations. - - Note that by this definition, DUT noiseless performance also - minimizes the impact of DUT fluctuations, as much as realistically - possible for a given trial duration. - - MLRsearch methodology aims to solve the DUT in SUT problem by - estimating the noiseless end of the SUT performance spectrum using a - limited number of trial results. - - Any improvements to the throughput search algorithm, aimed at better - dealing with software networking SUT and DUT setup, should employ - strategies recognizing the presence of SUT noise, allowing the - discovery of (proxies for) DUT noiseless performance at different - levels of sensitivity to SUT noise. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 7] - -Internet-Draft MLRsearch July 2024 - - -2.3. Repeatability and Comparability - - [RFC2544] does not suggest to repeat throughput search. And from - just one discovered throughput value, it cannot be determined how - repeatable that value is. Poor repeatability then leads to poor - comparability, as different benchmarking teams may obtain varying - throughput values for the same SUT, exceeding the expected - differences from search precision. - - [RFC2544] throughput requirements (60 seconds trial and no tolerance - of a single frame loss) affect the throughput results in the - following way. The SUT behavior close to the noiseful end of its - performance spectrum consists of rare occasions of significantly low - performance, but the long trial duration makes those occasions not so - rare on the trial level. Therefore, the binary search results tend - to wander away from the noiseless end of SUT performance spectrum, - more frequently and more widely than short trials would, thus causing - poor throughput repeatability. - - The repeatability problem can be addressed by defining a search - procedure that identifies a consistent level of performance, even if - it does not meet the strict definition of throughput in [RFC2544]. - - According to the SUT performance spectrum model, better repeatability - will be at the noiseless end of the spectrum. Therefore, solutions - to the DUT in SUT problem will help also with the repeatability - problem. - - Conversely, any alteration to [RFC2544] throughput search that - improves repeatability should be considered as less dependent on the - SUT noise. - - An alternative option is to simply run a search multiple times, and - report some statistics (e.g. average and standard deviation). This - can be used for a subset of tests deemed more important, but it makes - the search duration problem even more pronounced. - -2.4. Throughput with Non-Zero Loss - - [RFC1242] (section 3.17 Throughput) defines throughput as: The - maximum rate at which none of the offered frames are dropped by the - device. - - Then, it says: Since even the loss of one frame in a data stream can - cause significant delays while waiting for the higher level protocols - to time out, it is useful to know the actual maximum data rate that - the device can support. - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 8] - -Internet-Draft MLRsearch July 2024 - - - However, many benchmarking teams accept a small, non-zero loss ratio - as the goal for their load search. - - Motivations are many: - - * Modern protocols tolerate frame loss better, compared to the time - when [RFC1242] and [RFC2544] were specified. - - * Trials nowadays send way more frames within the same duration, - increasing the chance of a small SUT performance fluctuation being - enough to cause frame loss. - - * Small bursts of frame loss caused by noise have otherwise smaller - impact on the average frame loss ratio observed in the trial, as - during other parts of the same trial the SUT may work more closely - to its noiseless performance, thus perhaps lowering the Trial Loss - Ratio below the Goal Loss Ratio value. - - * If an approximation of the SUT noise impact on the Trial Loss - Ratio is known, it can be set as the Goal Loss Ratio. - - Regardless of the validity of all similar motivations, support for - non-zero loss goals makes any search algorithm more user-friendly. - [RFC2544] throughput is not user-friendly in this regard. - - Furthermore, allowing users to specify multiple loss ratio values, - and enabling a single search to find all relevant bounds, - significantly enhances the usefulness of the search algorithm. - - Searching for multiple Search Goals also helps to describe the SUT - performance spectrum better than the result of a single Search Goal. - For example, the repeated wide gap between zero and non-zero loss - loads indicates the noise has a large impact on the observed - performance, which is not evident from a single goal load search - procedure result. - - It is easy to modify the vanilla bisection to find a lower bound for - the intended load that satisfies a non-zero Goal Loss Ratio. But it - is not that obvious how to search for multiple goals at once, hence - the support for multiple Search Goals remains a problem. - -2.5. Inconsistent Trial Results - - While performing throughput search by executing a sequence of - measurement trials, there is a risk of encountering inconsistencies - between trial results. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 9] - -Internet-Draft MLRsearch July 2024 - - - The plain bisection never encounters inconsistent trials. But - [RFC2544] hints about the possibility of inconsistent trial results, - in two places in its text. The first place is section 24, where full - trial durations are required, presumably because they can be - inconsistent with the results from short trial durations. The second - place is section 26.3, where two successive zero-loss trials are - recommended, presumably because after one zero-loss trial there can - be a subsequent inconsistent non-zero-loss trial. - - Examples include: - - * A trial at the same load (same or different trial duration) - results in a different Trial Loss Ratio. - - * A trial at a higher load (same or different trial duration) - results in a smaller Trial Loss Ratio. - - Any robust throughput search algorithm needs to decide how to - continue the search in the presence of such inconsistencies. - Definitions of throughput in [RFC1242] and [RFC2544] are not specific - enough to imply a unique way of handling such inconsistencies. - - Ideally, there will be a definition of a new quantity which both - generalizes throughput for non-zero-loss (and other possible - repeatability enhancements), while being precise enough to force a - specific way to resolve trial result inconsistencies. But until such - a definition is agreed upon, the correct way to handle inconsistent - trial results remains an open problem. - -3. MLRsearch Specification - - This section describes MLRsearch specification including all - technical definitions needed for evaluating whether a particular test - procedure complies with MLRsearch specification. - -3.1. Overview - - MLRsearch specification describes a set of abstract system - components, acting as functions with specified inputs and outputs. - - A test procedure is said to comply with MLRsearch specification if it - can be conceptually divided into analogous components, each - satisfying requirements for the corresponding MLRsearch component. - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 10] - -Internet-Draft MLRsearch July 2024 - - - The Measurer component is tasked to perform trials, the Controller - component is tasked to select trial loads and durations, the Manager - component is tasked to pre-configure everything and to produce the - test report. The test report explicitly states Search Goals (as the - Controller Inputs) and corresponding Goal Results (Controller - Outputs). - - The Manager calls the Controller once, the Controller keeps calling - the Measurer until all stopping conditions are met. - - The part where Controller calls the Measurer is called the search. - Any activity done by the Manager before it calls the Controller (or - after Controller returns) is not considered to be part of the search. - - MLRsearch specification prescribes regular search results and - recommends their stopping conditions. Irregular search results are - also allowed, they may have different requirements and stopping - conditions. - - Search results are based on load classification. When measured - enough, any chosen load either achieves of fails each search goal, - thus becoming a lower or an upper bound for that goal. When the - relevant bounds are at loads that are close enough (according to goal - precision), the regular result is found. Search stops when all - regular results are found (or if some goals are proven to have only - irregular results). - -3.2. Measurement Quantities - - MLRsearch specification uses a number of measurement quantities. - - In general, MLRsearch specification does not require particular units - to be used, but it is REQUIRED for the test report to state all the - units. For example, ratio quantities can be dimensionless numbers - between zero and one, but may be expressed as percentages instead. - - For convenience, a group of quantities can be treated as a composite - quantity, One constituent of a composite quantity is called an - attribute, and a group of attribute values is called an instance of - that composite quantity. - - Some attributes are not independent from others, and they can be - calculated from other attributes. Such quantites are called derived - quantities. - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 11] - -Internet-Draft MLRsearch July 2024 - - -3.3. Existing Terms - - RFC 1242 "Benchmarking Terminology for Network Interconnect Devices" - contains basic definitions, and RFC 2544 "Benchmarking Methodology - for Network Interconnect Devices" contains discussions of a number of - terms and additional methodology requirements. RFC 2285 adds more - terms and discussions, describing some known situations in more - precise way. - - All three documents should be consulted before attempting to make use - of this document. - - Definitions of some central terms are copied and discussed in - subsections. - -3.3.1. SUT - - Defined in [RFC2285] (section 3.1.2 System Under Test (SUT)) as - follows. - - Definition: - - The collective set of network devices to which stimulus is offered as - a single entity and response measured. - - Discussion: - - An SUT consisting of a single network device is also allowed. - -3.3.2. DUT - - Defined in [RFC2285] (section 3.1.1 Device Under Test (DUT)) as - follows. - - Definition: - - The network forwarding device to which stimulus is offered and - response measured. - - Discussion: - - DUT, as a sub-component of SUT, is only indirectly mentioned in - MLRsearch specification, but is of key relevance for its motivation. - -3.3.3. Trial - - A trial is the part of the test described in [RFC2544] (section 23. - Trial description). - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 12] - -Internet-Draft MLRsearch July 2024 - - - Definition: - - A particular test consists of multiple trials. Each trial returns - one piece of information, for example the loss rate at a particular - input frame rate. Each trial consists of a number of phases: - - a) If the DUT is a router, send the routing update to the "input" - port and pause two seconds to be sure that the routing has settled. - - b) Send the "learning frames" to the "output" port and wait 2 seconds - to be sure that the learning has settled. Bridge learning frames are - frames with source addresses that are the same as the destination - addresses used by the test frames. Learning frames for other - protocols are used to prime the address resolution tables in the DUT. - The formats of the learning frame that should be used are shown in - the Test Frame Formats document. - - c) Run the test trial. - - d) Wait for two seconds for any residual frames to be received. - - e) Wait for at least five seconds for the DUT to restabilize. - - Discussion: - - The definition describes some traits, it is not clear whether all of - them are REQUIRED, or some of them are only RECOMMENDED. - - For the purposes of the MLRsearch specification, it is ALLOWED for - the test procedure to deviate from the [RFC2544] description, but any - such deviation MUST be made explicit in the test report. - - Trials are the only stimuli the SUT is expected to experience during - the search. - - In some discussion paragraphs, it is useful to consider the traffic - as sent and received by a tester, as implicitly defined in [RFC2544] - (section 6. Test set up). - - An example of deviation from [RFC2544] is using shorter wait times. - -3.4. Trial Terms - - This section defines new and redefine existing terms for quantities - relevant as inputs or outputs of trial, as used by the Measurer - component. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 13] - -Internet-Draft MLRsearch July 2024 - - -3.4.1. Trial Duration - - Definition: - - Trial duration is the intended duration of the traffic for a trial. - - Discussion: - - In general, this quantity does not include any preparation nor - waiting described in section 23 of [RFC2544] (section 23. Trial - description). - - While any positive real value may be provided, some Measurer - implementations MAY limit possible values, e.g. by rounding down to - neared integer in seconds. In that case, it is RECOMMENDED to give - such inputs to the Controller so the Controller only proposes the - accepted values. Alternatively, the test report MUST present the - rounded values as Search Goal attributes. - -3.4.2. Trial Load - - Definition: - - The trial load is the intended load for a trial - - Discussion: - - For test report purposes, it is assumed that this is a constant load - by default. This MAY be only an average load, e.g. when the traffic - is intended to be busty, e.g. as suggested in [RFC2544] (section 21. - Bursty traffic), but the test report MUST explicitly mention how non- - constant the traffic is. - - Trial load is the quantity defined as Constant Load of [RFC1242] - (section 3.4 Constant Load), Data Rate of [RFC2544] (section 14. - Bidirectional traffic) and Intended Load of [RFC2285] (section 3.5.1 - Intended load (Iload)). All three definitions specify that this - value applies to one (input or output) interface. - - For test report purposes, multi-interface aggregate load MAY be - reported, this is understood as the same quantity expressed using - different units. From the report it MUST be clear whether a - particular trial load value is per one interface, or an aggregate - over all interfaces. - - Similarly to trial duration, some Measurers may limit the possible - values of trial load. Contrary to trial duration, the test report is - NOT REQUIRED to document such behavior. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 14] - -Internet-Draft MLRsearch July 2024 - - - It is ALLOWED to combine trial load and trial duration in a way that - would not be possible to achieve using any integer number of data - frames. - -3.4.3. Trial Input - - Definition: - - Trial Input is a composite quantity, consisting of two attributes: - trial duration and trial load. - - Discussion: - - When talking about multiple trials, it is common to say "Trial - Inputs" to denote all corresponding Trial Input instances. - - A Trial Input instance acts as the input for one call of the Measurer - component. - - Contrary to other composite quantities, MLRsearch implementations are - NOT ALLOWED to add optional attributes here. This improves - interoperability between various implementations of the Controller - and the Measurer. - -3.4.4. Traffic Profile - - Definition: - - Traffic profile is a composite quantity containing attributes other - than trial load and trial duration, needed for unique determination - of the trial to be performed. - - Discussion: - - All its attributes are assumed to be constant during the search, and - the composite is configured on the Measurer by the Manager before the - search starts. This is why the traffic profile is not part of the - Trial Input. - - As a consequence, implementations of the Manager and the Measurer - must be aware of their common set of capabilities, so that the - traffic profile uniquely defines the traffic during the search. The - important fact is that none of those capabilities have to be known by - the Controller implementations. - - The traffic profile SHOULD contain some specific quantities, for - example [RFC2544] (section 9. Frame sizes) governs data link frame - size as defined in [RFC1242] (section 3.5 Data link frame size). - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 15] - -Internet-Draft MLRsearch July 2024 - - - Several more specific quantities may be RECOMMENDED, depending on - media type. For example, [RFC2544] (Appendix C) lists frame formats - and protocol addresses, as recommended from [RFC2544] (section 8. - Frame formats) and [RFC2544] (section 12. Protocol addresses). - - Depending on SUT configuration, e.g. when testing specific protocols, - additional attributes MUST be included in the traffic profile and in - the test report. - - Example: [RFC8219] (section 5.3. Traffic Setup) introduces traffic - setups consisting of a mix of IPv4 and IPv6 traffic - the implied - traffic profile therefore must include an attribute for their - percentage. - - Other traffic properties that need to be somehow specified in Traffic - Profile include: [RFC2544] (section 14. Bidirectional traffic), - [RFC2285] (section 3.3.3 Fully meshed traffic), and [RFC2544] - (section 11. Modifiers). - -3.4.5. Trial Forwarding Ratio - - Definition: - - The trial forwarding ratio is a dimensionless floating point value. - It MUST range between 0.0 and 1.0, both inclusive. It is calculated - by dividing the number of frames successfully forwarded by the SUT by - the total number of frames expected to be forwarded during the trial - - Discussion: - - For most traffic profiles, "expected to be forwarded" means "intended - to get transmitted from Tester towards SUT". - - Trial forwarding ratio MAY be expressed in other units (e.g. as a - percentage) in the test report. - - Note that, contrary to loads, frame counts used to compute trial - forwarding ratio are aggregates over all SUT output interfaces. - - Questions around what is the correct number of frames that should - have been forwarded is generally outside of the scope of this - document. - -3.4.6. Trial Loss Ratio - - Definition: - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 16] - -Internet-Draft MLRsearch July 2024 - - - The Trial Loss Ratio is equal to one minus the trial forwarding - ratio. - - Discussion: - - 100% minus the trial forwarding ratio, when expressed as a - percentage. - - This is almost identical to Frame Loss Rate of [RFC1242] (section 3.6 - Frame Loss Rate), the only minor difference is that Trial Loss Ratio - does not need to be expressed as a percentage. - -3.4.7. Trial Forwarding Rate - - Definition: - - The trial forwarding rate is a derived quantity, calculated by - multiplying the trial load by the trial forwarding ratio. - - Discussion: - - It is important to note that while similar, this quantity is not - identical to the Forwarding Rate as defined in [RFC2285] (section - 3.6.1 Forwarding rate (FR)). The latter is specific to one output - interface only, whereas the trial forwarding ratio is based on frame - counts aggregated over all SUT output interfaces. - -3.4.8. Trial Effective Duration - - Definition: - - Trial effective duration is a time quantity related to the trial, by - default equal to the trial duration. - - Discussion: - - This is an optional feature. If the Measurer does not return any - trial effective duration value, the Controller MUST use the trial - duration value instead. - - Trial effective duration may be any time quantity chosen by the - Measurer to be used for time-based decisions in the Controller. - - The test report MUST explain how the Measurer computes the returned - trial effective duration values, if they are not always equal to the - trial duration. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 17] - -Internet-Draft MLRsearch July 2024 - - - This feature can be beneficial for users who wish to manage the - overall search duration, rather than solely the traffic portion of - it. Simply measure the duration of the whole trial (waits including) - and use that as the trial effective duration. - - Also, this is a way for the Measurer to inform the Controller about - its surprising behavior, for example when rounding the trial duration - value. - -3.4.9. Trial Output - - Definition: - - Trial Output is a composite quantity. The REQUIRED attributes are - Trial Loss Ratio, trial effective duration and trial forwarding rate. - - Discussion: - - When talking about multiple trials, it is common to say "Trial - Outputs" to denote all corresponding Trial Output instances. - - Implementations may provide additional (optional) attributes. The - Controller implementations MUST ignore values of any optional - attribute they are not familiar with, except when passing Trial - Output instance to the Manager. - - Example of an optional attribute: The aggregate number of frames - expected to be forwarded during the trial, especially if it is not - just (a rounded-up value) implied by trial load and trial duration. - - While [RFC2285] (Section 3.5.2 Offered load (Oload)) requires the - offered load value to be reported for forwarding rate measurements, - it is NOT REQUIRED in MLRsearch specification. - -3.4.10. Trial Result - - Definition: - - Trial result is a composite quantity, consisting of the Trial Input - and the Trial Output. - - Discussion: - - When talking about multiple trials, it is common to say "trial - results" to denote all corresponding trial result instances. - - While implementations SHOULD NOT include additional attributes with - independent values, they MAY include derived quantities. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 18] - -Internet-Draft MLRsearch July 2024 - - -3.5. Goal Terms - - This section defines new and redefine existing terms for quantities - indirectly relevant for inputs or outputs of the Controller - component. - - Several goal attributes are defined before introducing the main - component quantity: the Search Goal. - -3.5.1. Goal Final Trial Duration - - Definition: - - A threshold value for trial durations. - - Discussion: - - This attribute value MUST be positive. - - A trial with Trial Duration at least as long as the Goal Final Trial - Duration is called a full-length trial (with respect to the given - Search Goal). - - A trial that is not full-length is called a short trial. - - Informally, while MLRsearch is allowed to perform short trials, the - results from such short trials have only limited impact on search - results. - - One trial may be full-length for some Search Goals, but not for - others. - - The full relation of this goal to Controller Output is defined later - in this document in subsections of [Goal Result] (#Goal-Result). For - example, the Conditional Throughput for this goal is computed only - from full-length trial results. - -3.5.2. Goal Duration Sum - - Definition: - - A threshold value for a particular sum of trial effective durations. - - Discussion: - - This attribute value MUST be positive. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 19] - -Internet-Draft MLRsearch July 2024 - - - Informally, even when looking only at full-length trials, MLRsearch - may spend up to this time measuring the same load value. - - If the Goal Duration Sum is larger than the Goal Final Trial - Duration, multiple full-length trials may need to be performed at the - same load. - - See [TST009 Example] (#TST009-Example) for an example where - possibility of multiple full-length trials at the same load is - intended. - - A Goal Duration Sum value lower than the Goal Final Trial Duration - (of the same goal) could save some search time, but is NOT - RECOMMENDED. See [Relevant Upper Bound] (#Relevant-Upper-Bound) for - partial explanation. - -3.5.3. Goal Loss Ratio - - Definition: - - A threshold value for Trial Loss Ratios. - - Discussion: - - Attribute value MUST be non-negative and smaller than one. - - A trial with Trial Loss Ratio larger than a Goal Loss Ratio value is - called a lossy trial, with respect to given Search Goal. - - Informally, if a load causes too many lossy trials, the Relevant - Lower Bound for this goal will be smaller than that load. - - If a trial is not lossy, it is called a low-loss trial, or - (specifically for zero Goal Loss Ratio value) zero-loss trial. - -3.5.4. Goal Exceed Ratio - - Definition: - - A threshold value for a particular ratio of sums of Trial Effective - Durations. - - Discussion: - - Attribute value MUST be non-negative and smaller than one. - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 20] - -Internet-Draft MLRsearch July 2024 - - - See later sections for details on which sums. Specifically, the - direct usage is only in [Appendix A: Load Classification] (#Appendix- - A:-Load-Classification) and [Appendix B: Conditional Throughput] - (#Appendix-B:-Conditional-Throughput). The impact of that usage is - discussed in subsections leading to [Goal Result] (#Goal-Result). - - Informally, the impact of lossy trials is controlled by this value. - Effectively, Goal Exceed Ratio is a percentage of full-length trials - that may be lossy without the load being classified as the [Relevant - Upper Bound] (#Relevant-Upper-Bound). - -3.5.5. Goal Width - - Definition: - - A value used as a threshold for deciding whether two trial load - values are close enough. - - Discussion: - - If present, the value MUST be positive. - - Informally, this acts as a stopping condition, controlling the - precision of the search. The search stops if every goal has reached - its precision. - - Implementations without this attribute MUST give the Controller other - ways to control the search stopping conditions. - - Absolute load difference and relative load difference are two popular - choices, but implementations may choose a different way to specify - width. - - The test report MUST make it clear what specific quantity is used as - Goal Width. - - It is RECOMMENDED to set the Goal Width (as relative difference) - value to a value no smaller than the Goal Loss Ratio. (The reason is - not obvious, see [Throughput] (#Throughput) if interested.) - -3.5.6. Search Goal - - Definition: - - The Search Goal is a composite quantity consisting of several - attributes, some of them are required. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 21] - -Internet-Draft MLRsearch July 2024 - - - Required attributes: - Goal Final Trial Duration - Goal Duration Sum - - Goal Loss Ratio - Goal Exceed Ratio - - Optional attribute: - Goal Width - - Discussion: - - Implementations MAY add their own attributes. Those additional - attributes may be required by the implementation even if they are not - required by MLRsearch specification. But it is RECOMMENDED for those - implementations to support missing values by computing reasonable - defaults. - - The meaning of listed attributes is formally given only by their - indirect effect on the search results. - - Informally, later sections provide additional intuitions and examples - of the Search Goal attribute values. - - An example of additional attributes required by some implementations - is Goal Initial Trial Duration, together with another attribute that - controls possible intermediate Trial Duration values. The reasonable - default in this case is using the Goal Final Trial Duration and no - intermediate values. - -3.5.7. Controller Input - - Definition: - - Controller Input is a composite quantity required as an input for the - Controller. The only REQUIRED attribute is a list of Search Goal - instances. - - Discussion: - - MLRsearch implementations MAY use additional attributes. Those - additional attributes may be required by the implementation even if - they are not required by MLRsearch specification. - - Formally, the Manager does not apply any Controller configuration - apart from one Controller Input instance. - - For example, Traffic Profile is configured on the Measurer by the - Manager (without explicit assistance of the Controller). - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 22] - -Internet-Draft MLRsearch July 2024 - - - The order of Search Goal instances in a list SHOULD NOT have a big - impact on Controller Output (see section [Controller Output] - (#Controller-Output) , but MLRsearch implementations MAY base their - behavior on the order of Search Goal instances in a list. - - An example of an optional attribute (outside the list of Search - Goals) required by some implementations is Max Load. While this is a - frequently used configuration parameter, already governed by - [RFC2544] (section 20. Maximum frame rate) and [RFC2285] (3.5.3 - Maximum offered load (MOL)), some implementations may detect or - discover it instead. - - In MLRsearch specification, the [Relevant Upper Bound] (#Relevant- - Upper-Bound) is added as a required attribute precisely because it - makes the search result independent of Max Load value. - -3.6. Search Goal Examples - -3.6.1. RFC2544 Goal - - The following set of values makes the search result unconditionally - compliant with [RFC2544] (section 24 Trial duration) - - * Goal Final Trial Duration = 60 seconds - - * Goal Duration Sum = 60 seconds - - * Goal Loss Ratio = 0% - - * Goal Exceed Ratio = 0% - - The latter two attributes are enough to make the search goal - conditionally compliant, adding the first attribute makes it - unconditionally compliant. - - The second attribute (Goal Duration Sum) only prevents MLRsearch from - repeating zero-loss full-length trials. - - Non-zero exceed ratio could prolong the search and allow loss - inversion between lower-load lossy short trial and higher-load full- - length zero-loss trial. From [RFC2544] alone, it is not clear - whether that higher load could be considered as compliant throughput. - - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 23] - -Internet-Draft MLRsearch July 2024 - - -3.6.2. TST009 Goal - - One of the alternatives to RFC2544 is described in [TST009] (section - 12.3.3 Binary search with loss verification). The idea there is to - repeat lossy trials, hoping for zero loss on second try, so the - results are closer to the noiseless end of performance sprectum, and - more repeatable and comparable. - - Only the variant with "z = infinity" is achievable with MLRsearch. - - For example, for "r = 2" variant, the following search goal should be - used: - - * Goal Final Trial Duration = 60 seconds - - * Goal Duration Sum = 120 seconds - - * Goal Loss Ratio = 0% - - * Goal Exceed Ratio = 50% - - If the first 60s trial has zero loss, it is enough for MLRsearch to - stop measuring at that load, as even a second lossy trial would still - fit within the exceed ratio. - - But if the first trial is lossy, MLRsearch needs to perform also the - second trial to classify that load. As Goal Duration Sum is twice as - long as Goal Final Trial Duration, third full-length trial is never - needed. - -3.7. Result Terms - - Before defining the output of the Controller, it is useful to define - what the Goal Result is. - - The Goal Result is a composite quantity. - - Following subsections define its attribute first, before describing - the Goal Result quantity. - - There is a correspondence between Search Goals and Goal Results. - Most of the following subsections refer to a given Search Goal, when - defining attributes of the Goal Result. Conversely, at the end of - the search, each Search Goal has its corresponding Goal Result. - - Conceptually, the search can be seen as a process of load - classification, where the Controller attempts to classify some loads - as an Upper Bound or a Lower Bound with respect to some Search Goal. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 24] - -Internet-Draft MLRsearch July 2024 - - - Before defining real attributes of the goal result, it is useful to - define bounds in general. - -3.7.1. Relevant Upper Bound - - Definition: - - The Relevant Upper Bound is the smallest trial load value that is - classified at the end of the search as an upper bound (see - [Appendix A: Load Classification] (#Appendix-A:-Load-Classification)) - for the given Search Goal. - - Discussion: - - One search goal can have many different load classified as an upper - bound. At the end of the search, one of those loads will be the - smallest, becoming the relevant upper bound for that goal. - - In more detail, the set of all trial outputs (both short and full- - length, enough of them according to Goal Duration Sum) performed at - that smallest load failed to uphold all the requirements of the given - Search Goal, mainly the Goal Loss Ratio in combination with the Goal - Exceed Ratio. - - If Max Load does not cause enough lossy trials, the Relevant Upper - Bound does not exist. Conversely, if Relevant Upper Bound exists, it - is not affected by Max Load value. - -3.7.2. Relevant Lower Bound - - Definition: - - The Relevant Lower Bound is the largest trial load value among those - smaller than the Relevant Upper Bound, that got classified at the end - of the search as a lower bound (see [Appendix A: Load Classification] - (#Appendix-A:-Load-Classification)) for the given Search Goal. - - Discussion: - - Only among loads smaller that the relevant upper bound, the largest - load becomes the relevant lower bound. With loss inversion, stricter - upper bound matters. - - In more detail, the set of all trial outputs (both short and full- - length, enough of them according to Goal Duration Sum) performed at - that largest load managed to uphold all the requirements of the given - Search Goal, mainly the Goal Loss Ratio in combination with the Goal - Exceed Ratio. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 25] - -Internet-Draft MLRsearch July 2024 - - - Is no load had enough low-loss trials, the relevant lower bound MAY - not exist. - - Strictly speaking, if the Relevant Upper Bound does not exist, the - Relevant Lower Bound also does not exist. In that case, Max Load is - classified as a lower bound, but it is not clear whether a higher - lower bound would be found if the search used a higher Max Load - value. - - For a regular Goal Result, the distance between the Relevant Lower - Bound and the Relevant Upper Bound MUST NOT be larger than the Goal - Width, if the implementation offers width as a goal attribute. - - Searching for anther search goal may cause a loss inversion - phenomenon, where a lower load is classified as an upper bound, but - also a higher load is classified as a lower bound for the same search - goal. The definition of the Relevant Lower Bound ignores such high - lower bounds. - -3.7.3. Conditional Throughput - - Definition: - - The Conditional Throughput (see section [Appendix B: Conditional - Throughput] (#Appendix-B:-Conditional-Throughput)) as evaluated at - the Relevant Lower Bound of the given Search Goal at the end of the - search. - - Discussion: - - Informally, this is a typical trial forwarding rate, expected to be - seen at the Relevant Lower Bound of the given Search Goal. - - But frequently it is only a conservative estimate thereof, as - MLRsearch implementations tend to stop gathering more data as soon as - they confirm the value cannot get worse than this estimate within the - Goal Duration Sum. - - This value is RECOMMENDED to be used when evaluating repeatability - and comparability if different MLRsearch implementations. - -3.7.4. Goal Result - - Definition: - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 26] - -Internet-Draft MLRsearch July 2024 - - - The Goal Result is a composite quantity consisting of several - attributes. Relevant Upper Bound and Relevant Lower Bound are - REQUIRED attributes, Conditional Throughput is a RECOMMENDED - attribute. - - Discussion: - - Depending on SUT behavior, it is possible that one or both relevant - bounds do not exist. The goal result instance where the required - attribute values exist is informally called a Regular Goal Result - instance, so we can say some goals reached Irregular Goal Results. - - A typical Irregular Goal Result is when all trials at the Max Load - have zero loss, as the Relevant Upper Bound does not exist in that - case. - - It is RECOMMENDED that the test report will display such results - appropriately, although MLRsearch specification does not prescibe - how. - - Anything else regarging Irregular Goal Results, including their role - in stopping conditions of the search is outside the scope of this - document. - -3.7.5. Search Result - - Definition: - - The Search Result is a single composite object that maps each Search - Goal instance to a corresponding Goal Result instance. - - Discussion: - - Alternatively, the Search Result can be implemented as an ordered - list of the Goal Result instances, matching the order of Search Goal - instances. - - The Search Result (as a mapping) MUST map from all the Search Goal - instances present in the Controller Input. - -3.7.6. Controller Output - - Definition: - - The Controller Output is a composite quantity returned from the - Controller to the Manager at the end of the search. The Search - Result instance is its only REQUIRED attribute. - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 27] - -Internet-Draft MLRsearch July 2024 - - - Discussion: - - MLRsearch implementation MAY return additional data in the Controller - Output. - -3.8. MLRsearch Architecture - - MLRsearch architecture consists of three main system components: the - Manager, the Controller, and the Measurer. - - The architecture also implies the presence of other components, such - as the SUT and the Tester (as a sub-component of the Measurer). - - Protocols of communication between components are generally left - unspecified. For example, when MLRsearch specification mentions - "Controller calls Measurer", it is possible that the Controller - notifies the Manager to call the Measurer indirectly instead. This - way the Measurer implementations can be fully independent from the - Controller implementations, e.g. programmed in different programming - languages. - -3.8.1. Measurer - - Definition: - - The Measurer is an abstract system component that when called with a - [Trial Input] (#Trial-Input) instance, performs one [Trial] (#Trial), - and returns a [Trial Output] (#Trial-Output) instance. - - Discussion: - - This definition assumes the Measurer is already initialized. In - practice, there may be additional steps before the search, e.g. when - the Manager configures the traffic profile (either on the Measurer or - on its tester sub-component directly) and performs a warmup (if the - tester requires one). - - It is the responsibility of the Measurer implementation to uphold any - requirements and assumptions present in MLRsearch specification, e.g. - trial forwarding ratio not being larger than one. - - Implementers have some freedom. For example [RFC2544] (section 10. - Verifying received frames) gives some suggestions (but not - requirements) related to duplicated or reordered frames. - Implementations are RECOMMENDED to document their behavior related to - such freedoms in as detailed a way as possible. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 28] - -Internet-Draft MLRsearch July 2024 - - - It is RECOMMENDED to benchmark the test equipment first, e.g. connect - sender and receiver directly (without any SUT in the path), find a - load value that guarantees the offered load is not too far from the - intended load, and use that value as the Max Load value. When - testing the real SUT, it is RECOMMENDED to turn any big difference - between the intended load and the offered load into increased Trial - Loss Ratio. - - Neither of the two recommendations are made into requirements, - because it is not easy to tell when the difference is big enough, in - a way thay would be dis-entangled from other Measurer freedoms. - -3.8.2. Controller - - Definition: - - The Controller is an abstract system component that when called with - a Controller Input instance repeatedly computes Trial Input instance - for the Measurer, obtains corresponding Trial Output instances, and - eventually returns a Controller Output instance. - - Discussion: - - Informally, the Controller has big freedom in selection of Trial - Inputs, and the implementations want to achieve the Search Goals in - the shortest expected time. - - The Controller's role in optimizing the overall search time - distinguishes MLRsearch algorithms from simpler search procedures. - - Informally, each implementation can have different stopping - conditions. Goal Width is only one example. In practice, - implementation details do not matter, as long as Goal Results are - regular. - -3.8.3. Manager - - Definition: - - The Manager is an abstract system component that is reponsible for - configuring other components, calling the Controller component once, - and for creating the test report following the reporting format as - defined in [RFC2544] (section 26. Benchmarking tests). - - Discussion: - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 29] - -Internet-Draft MLRsearch July 2024 - - - The Manager initializes the SUT, the Measurer (and the Tester if - independent) with their intended configurations before calling the - Controller. - - The Manager does not need to be able to tweak any Search Goal - attributes, but it MUST report all applied attribute values even if - not tweaked. - - In principle, there should be a "user" (human or CI) that "starts" or - "calls" the Manager and receives the report. The Manager MAY be able - to be called more than once whis way. - -3.9. Implementation Compliance - - Any networking measurement setup where there can be logically - delineated system components and there are components satisfying - requirements for the Measurer, the Controller and the Manager, is - considered to be compliant with MLRsearch design. - - These components can be seen as abstractions present in any testing - procedure. For example, there can be a single component acting both - as the Manager and the Controller, but as long as values of required - attributes of Search Goals and Goal Results are visible in the test - report, the Controller Input instance and output instance are - implied. - - For example, any setup for conditionally (or unconditionally) - compliant [RFC2544] throughput testing can be understood as a - MLRsearch architecture, assuming there is enough data to reconstruct - the Relevant Upper Bound. - - See [RFC2544 Goal] (#RFC2544-Goal) subsection for equivalent Search - Goal. - - Any test procedure that can be understood as (one call to the Manager - of) MLRsearch architecture is said to be compliant with MLRsearch - specification. - -4. Additional Considerations - - This section focuses on additional considerations, intuitions and - motivations pertaining to MLRsearch methodology. - - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 30] - -Internet-Draft MLRsearch July 2024 - - -4.1. MLRsearch Versions - - The MLRsearch algorithm has been developed in a code-first approach, - a Python library has been created, debugged, used in production and - published in PyPI before the first descriptions (even informal) were - published. - - But the code (and hence the description) was evolving over time. - Multiple versions of the library were used over past several years, - and later code was usually not compatible with earlier descriptions. - - The code in (some version of) MLRsearch library fully determines the - search process (for a given set of configuration parameters), leaving - no space for deviations. - - This historic meaning of MLRsearch, as a family of search algorithm - implementations, leaves plenty of space for future improvements, at - the cost of poor comparability of results of search algoritm - implementations. - - There are two competing needs. There is the need for standardization - in areas critical to comparability. There is also the need to allow - flexibility for implementations to innovate and improve in other - areas. This document defines MLRsearch as a new specification in a - manner that aims to fairly balance both needs. - -4.2. Stopping Conditions - - [RFC2544] prescribes that after performing one trial at a specific - offered load, the next offered load should be larger or smaller, - based on frame loss. - - The usual implementation uses binary search. Here a lossy trial - becomes a new upper bound, a lossless trial becomes a new lower - bound. The span of values between the tightest lower bound and the - tightest upper bound (including both values) forms an interval of - possible results, and after each trial the width of that interval - halves. - - Usually the binary search implementation tracks only the two tightest - bounds, simply calling them bounds. But the old values still remain - valid bounds, just not as tight as the new ones. - - After some number of trials, the tightest lower bound becomes the - throughput. [RFC2544] does not specify when, if ever, should the - search stop. - - MLRsearch introduces a concept of [Goal Width] (#Goal-Width). - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 31] - -Internet-Draft MLRsearch July 2024 - - - The search stops when the distance between the tightest upper bound - and the tightest lower bound is smaller than a user-configured value, - called Goal Width from now on. In other words, the interval width at - the end of the search has to be no larger than the Goal Width. - - This Goal Width value therefore determines the precision of the - result. Due to the fact that MLRsearch specification requires a - particular structure of the result (see [Trial Result] (#Trial- - Result) section), the result itself does contain enough information - to determine its precision, thus it is not required to report the - Goal Width value. - - This allows MLRsearch implementations to use stopping conditions - different from Goal Width. - -4.3. Load Classification - - MLRsearch keeps the basic logic of binary search (tracking tightest - bounds, measuring at the middle), perhaps with minor technical - differences. - - MLRsearch algorithm chooses an intended load (as opposed to the - offered load), the interval between bounds does not need to be split - exactly into two equal halves, and the final reported structure - specifies both bounds. - - The biggest difference is that to classify a load as an upper or - lower bound, MLRsearch may need more than one trial (depending on - configuration options) to be performed at the same intended load. - - In consequence, even if a load already does have few trial results, - it still may be classified as undecided, neither a lower bound nor an - upper bound. - - An explanation of the classification logic is given in the next - section [Logic of Load Classification] (#Logic-of-Load- - Classification), as it heavily relies on other subsections of this - section. - - For repeatability and comparability reasons, it is important that - given a set of trial results, all implementations of MLRsearch - classify the load equivalently. - -4.4. Loss Ratios - - Another difference between MLRsearch and [RFC2544] binary search is - in the goals of the search. [RFC2544] has a single goal, based on - classifying full-length trials as either lossless or lossy. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 32] - -Internet-Draft MLRsearch July 2024 - - - MLRsearch, as the name suggests, can search for multiple goals, - differing in their loss ratios. The precise definition of the Goal - Loss Ratio will be given later. The [RFC2544] throughput goal then - simply becomes a zero Goal Loss Ratio. Different goals also may have - different Goal Widths. - - A set of trial results for one specific intended load value can - classify the load as an upper bound for some goals, but a lower bound - for some other goals, and undecided for the rest of the goals. - - Therefore, the load classification depends not only on trial results, - but also on the goal. The overall search procedure becomes more - complicated, when compared to binary search with a single goal, but - most of the complications do not affect the final result, except for - one phenomenon, loss inversion. - -4.5. Loss Inversion - - In [RFC2544] throughput search using bisection, any load with a lossy - trial becomes a hard upper bound, meaning every subsequent trial has - a smaller intended load. - - But in MLRsearch, a load that is classified as an upper bound for one - goal may still be a lower bound for another goal, and due to the - other goal MLRsearch will probably perform trials at even higher - loads. What to do when all such higher load trials happen to have - zero loss? Does it mean the earlier upper bound was not real? Does - it mean the later lossless trials are not considered a lower bound? - Surely we do not want to have an upper bound at a load smaller than a - lower bound. - - MLRsearch is conservative in these situations. The upper bound is - considered real, and the lossless trials at higher loads are - considered to be a coincidence, at least when computing the final - result. - - This is formalized using new notions, the [Relevant Upper Bound] - (#Relevant-Upper-Bound) and the [Relevant Lower Bound] (#Relevant- - Lower-Bound). Load classification is still based just on the set of - trial results at a given intended load (trials at other loads are - ignored), making it possible to have a lower load classified as an - upper bound, and a higher load classified as a lower bound (for the - same goal). The Relevant Upper Bound (for a goal) is the smallest - load classified as an upper bound. But the Relevant Lower Bound is - not simply the largest among lower bounds. It is the largest load - among loads that are lower bounds while also being smaller than the - Relevant Upper Bound. - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 33] - -Internet-Draft MLRsearch July 2024 - - - With these definitions, the Relevant Lower Bound is always smaller - than the Relevant Upper Bound (if both exist), and the two relevant - bounds are used analogously as the two tightest bounds in the binary - search. When they are less than the Goal Width apart, the relevant - bounds are used in the output. - - One consequence is that every trial result can have an impact on the - search result. That means if your SUT (or your traffic generator) - needs a warmup, be sure to warm it up before starting the search. - -4.6. Exceed Ratio - - The idea of performing multiple trials at the same load comes from a - model where some trial results (those with high loss) are affected by - infrequent effects, causing poor repeatability of [RFC2544] - throughput results. See the discussion about noiseful and noiseless - ends of the SUT performance spectrum in section [DUT in SUT] (#DUT- - in-SUT). Stable results are closer to the noiseless end of the SUT - performance spectrum, so MLRsearch may need to allow some frequency - of high-loss trials to ignore the rare but big effects near the - noiseful end. - - MLRsearch can do such trial result filtering, but it needs a - configuration option to tell it how frequent can the infrequent big - loss be. This option is called the exceed ratio. It tells MLRsearch - what ratio of trials (more exactly what ratio of trial seconds) can - have a [Trial Loss Ratio] (#Trial-Loss-Ratio) larger than the Goal - Loss Ratio and still be classified as a lower bound. Zero exceed - ratio means all trials have to have a Trial Loss Ratio equal to or - smaller than the Goal Loss Ratio. - - For explainability reasons, the RECOMMENDED value for exceed ratio is - 0.5, as it simplifies some later concepts by relating them to the - concept of median. - -4.7. Duration Sum - - When more than one trial is intended to classify a load, MLRsearch - also needs something that controls the number of trials needed. - Therefore, each goal also has an attribute called duration sum. - - The meaning of a [Goal Duration Sum] (#Goal-Duration-Sum) is that - when a load has (full-length) trials whose trial durations when - summed up give a value at least as big as the Goal Duration Sum - value, the load is guaranteed to be classified either as an upper - bound or a lower bound for that goal. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 34] - -Internet-Draft MLRsearch July 2024 - - - Due to the fact that the duration sum has a big impact on the overall - search duration, and [RFC2544] prescribes wait intervals around trial - traffic, the MLRsearch algorithm is allowed to sum durations that are - different from the actual trial traffic durations. - - In the MLRsearch specification, the different duration values are - called [Trial Effective Duration] (#Trial-Effective-Duration). - -4.8. Short Trials - - MLRsearch requires each goal to specify its final trial duration. - Full-length trial is a shorter name for a trial whose intended trial - duration is equal to (or longer than) the goal final trial duration. - - Section 24 of [RFC2544] already anticipates possible time savings - when short trials (shorter than full-length trials) are used. Full- - length trials are the opposite of short trials, so they may also be - called long trials. - - Any MLRsearch implementation may include its own configuration - options which control when and how MLRsearch chooses to use short - trial durations. - - For explainability reasons, when exceed ratio of 0.5 is used, it is - recommended for the Goal Duration Sum to be an odd multiple of the - full trial durations, so Conditional Throughput becomes identical to - a median of a particular set of trial forwarding rates. - - The presence of short trial results complicates the load - classification logic. - - Full details are given later in section [Logic of Load - Classification] (#Logic-of-Load-Classification). In a nutshell, - results from short trials may cause a load to be classified as an - upper bound. This may cause loss inversion, and thus lower the - Relevant Lower Bound, below what would classification say when - considering full-length trials only. - -4.9. Throughput - - Due to the fact that testing equipment takes the intended load as an - input parameter for a trial measurement, any load search algorithm - needs to deal with intended load values internally. - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 35] - -Internet-Draft MLRsearch July 2024 - - - But in the presence of goals with a non-zero loss ratio, the intended - load usually does not match the user's intuition of what a throughput - is. The forwarding rate (as defined in [RFC2285] section 3.6.1) is - better, but it is not obvious how to generalize it for loads with - multiple trial results and a non-zero [Goal Loss Ratio] (#Goal-Loss- - Ratio). - - The best example is also the main motivation: hard limit performance. - Even if the medium allows higher performance, the SUT interfaces may - have their additional own limitations, e.g. a specific fps limit on - the NIC (a very common occurance). - - Ideally, those should be known and used when computing Max Load. But - if Max Load is higher that what interface can receive or transmit, - there will be a "hard limit" observed in trial results. Imagine the - hard limit is at 100 Mfps, Max Load is higher, and the goal loss - ratio is 0.5%. If DUT has no additional losses, 0.5% loss ratio will - be achieved at 100.5025 Mfps (the relevant lower bound). But it is - not intuitive to report SUT performance as a value that is larger - than known hard limit. We need a generalization of RFC2544 - throughput, different from just the relevant lower bound. - - MLRsearch defines one such generalization, called the Conditional - Throughput. It is the trial forwarding rate from one of the trials - performed at the load in question. Determining which trial exactly - is defined in [MLRsearch Specification] (#MLRsearch-Specification), - and in [Appendix B: Conditional Throughput] (#Appendix-B:- - Conditional-Throughput). - - In the hard limit example, 100.5 Mfps load will still have only 100.0 - Mfps forwarding rate, nicely confirming the known limitation. - - Conditional Throughput is partially related to load classification. - If a load is classified as a lower bound for a goal, the Conditional - Throughput can be calculated from trial results, and guaranteed to - show an loss ratio no larger than the Goal Loss Ratio. - - Note that when comparing the best (all zero loss) and worst case (all - loss just below Goal Loss Ratio), the same Relevant Lower Bound value - may result in the Conditional Throughput differing up to the Goal - Loss Ratio. - - - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 36] - -Internet-Draft MLRsearch July 2024 - - - Therefore it is rarely needed to set the Goal Width (if expressed as - the relative difference of loads) below the Goal Loss Ratio. In - other words, setting the Goal Width below the Goal Loss Ratio may - cause the Conditional Throughput for a larger loss ratio to become - smaller than a Conditional Throughput for a goal with a smaller Goal - Loss Ratio, which is counter-intuitive, considering they come from - the same search. Therefore it is RECOMMENDED to set the Goal Width - to a value no smaller than the Goal Loss Ratio. - - Overall, this Conditional Throughput does behave well for - comparability purposes. - -4.10. Search Time - - MLRsearch was primarily developed to reduce the time required to - determine a throughput, either the [RFC2544] compliant one, or some - generalization thereof. The art of achieving short search times is - mainly in the smart selection of intended loads (and intended - durations) for the next trial to perform. - - While there is an indirect impact of the load selection on the - reported values, in practice such impact tends to be small, even for - SUTs with quite a broad performance spectrum. - - A typical example of two approaches to load selection leading to - different Relevant Lower Bounds is when the interval is split in a - very uneven way. Any implementation choosing loads very close to the - current Relevant Lower Bound is quite likely to eventually stumble - upon a trial result with poor performance (due to SUT noise). For an - implementation choosing loads very close to the current Relevant - Upper Bound, this is unlikely, as it examines more loads that can see - a performance close to the noiseless end of the SUT performance - spectrum. - - However, as even splits optimize search duration at give precision, - MLRsearch implementations that prioritize minimizing search time are - unlikely to suffer from any such bias. - - Therefore, this document remains quite vague on load selection and - other optimization details, and configuration attributes related to - them. Assuming users prefer libraries that achieve short overall - search time, the definition of the Relevant Lower Bound should be - strict enough to ensure result repeatability and comparability - between different implementations, while not restricting future - implementations much. - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 37] - -Internet-Draft MLRsearch July 2024 - - -4.11. [RFC2544] Compliance - - Some Search Goal instances lead to results compliant with RFC2544. - See [RFC2544 Goal] (#RFC2544-Goal) for more details regarding both - conditional and unconditional compliance. - - The presence of other Search Goals does not affect the compliance of - this Goal Result. The Relevant Lower Bound and the Conditional - Throughput are in this case equal to each other, and the value is the - [RFC2544] throughput. - -5. Logic of Load Classification - -5.1. Introductory Remarks - - This chapter continues with explanations, but this time more precise - definitions are needed for readers to follow the explanations. - - Descriptions in this section are wordy and implementers should read - [MLRsearch Specification] (#MLRsearch-Specification) section and - Appendices for more concise definitions. - - The two areas of focus here are load classification and the - Conditional Throughput. - - To start with [Performance Spectrum] (#Performance-Spectrum) - subsection contains definitions needed to gain insight into what - Conditional Throughput means. Remaining subsections discuss load - classification. - - For load classification, it is useful to define *good trials* and - *bad trials*: - - * *Bad trial*: Trial is called bad (according to a goal) if its - [Trial Loss Ratio] (#Trial-Loss-Ratio) is larger than the [Goal - Loss Ratio] (#Goal-Loss-Ratio). - - * *Good trial*: Trial that is not bad is called good. - -5.2. Performance Spectrum - - ### Description - - There are several equivalent ways to explain the Conditional - Throughput computation. One of the ways relies on performance - spectrum. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 38] - -Internet-Draft MLRsearch July 2024 - - - Take an intended load value, a trial duration value, and a finite set - of trial results, with all trials measured at that load value and - duration value. - - The performance spectrum is the function that maps any non-negative - real number into a sum of trial durations among all trials in the - set, that has that number, as their trial forwarding rate, e.g. map - to zero if no trial has that particular forwarding rate. - - A related function, defined if there is at least one trial in the - set, is the performance spectrum divided by the sum of the durations - of all trials in the set. - - That function is called the performance probability function, as it - satisfies all the requirements for probability mass function of a - discrete probability distribution, the one-dimensional random - variable being the trial forwarding rate. - - These functions are related to the SUT performance spectrum, as - sampled by the trials in the set. - - Take a set of all full-length trials performed at the Relevant Lower - Bound, sorted by decreasing trial forwarding rate. The sum of the - durations of those trials may be less than the Goal Duration Sum, or - not. If it is less, add an imaginary trial result with zero trial - forwarding rate, such that the new sum of durations is equal to the - Goal Duration Sum. This is the set of trials to use. - - If the quantile touches two trials, - - the larger trial forwarding rate (from the trial result sorted - earlier) is used. - - The resulting quantity is the Conditional Throughput of the goal in - question. - - A set of examples follows. - -5.2.1. First Example - - * [Goal Exceed Ratio] (#Goal-Exceed-Ratio) = 0 and [Goal Duration - Sum] (#Goal-Duration-Sum) has been reached. - - * Conditional Throughput is the smallest trial forwarding rate among - the trials. - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 39] - -Internet-Draft MLRsearch July 2024 - - -5.2.2. Second Example - - * Goal Exceed Ratio = 0 and Goal Duration Sum has not been reached - yet. - - * Due to the missing duration sum, the worst case may still happen, - so the Conditional Throughput is zero. - - * This is not reported to the user, as this load cannot become the - Relevant Lower Bound yet. - -5.2.3. Third Example - - * Goal Exceed Ratio = 50% and Goal Duration Sum is two seconds. - - * One trial is present with the duration of one second and zero - loss. - - * The imaginary trial is added with the duration of one second and - zero trial forwarding rate. - - * The median would touch both trials, so the Conditional Throughput - is the trial forwarding rate of the one non-imaginary trial. - - * As that had zero loss, the value is equal to the offered load. - -5.2.4. Summary - - While the Conditional Throughput is a generalization of the trial - forwarding rate, its definition is not an obvious one. - - Other than the trial forwarding rate, the other source of intuition - is the quantile in general, and the median the recommended case. - -5.3. Trials with Single Duration - - When goal attributes are chosen in such a way that every trial has - the same intended duration, the load classification is simpler. - - The following description follows the motivation of Goal Loss Ratio, - Goal Exceed Ratio, and Goal Duration Sum. - - If the sum of the durations of all trials (at the given load) is less - than the Goal Duration Sum, imagine two scenarios: - - * *best case scenario*: all subsequent trials having zero loss, and - - * *worst case scenario*: all subsequent trials having 100% loss. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 40] - -Internet-Draft MLRsearch July 2024 - - - Here we assume there are as many subsequent trials as needed to make - the sum of all trials equal to the Goal Duration Sum. - - The exceed ratio is defined using sums of durations (and number of - trials does not matter), so it does not matter whether the - "subsequent trials" can consist of an integer number of full-length - trials. - - In any of the two scenarios, best case and worst case, we can compute - the load exceed ratio, as the duration sum of good trials divided by - the duration sum of all trials, in both cases including the assumed - trials. - - Even if, in the best case scenario, the load exceed ratio is larger - than the Goal Exceed Ratio, the load is an upper bound. - - MKP2 Even if, in the worst case scenario, the load exceed ratio is - not larger than the Goal Exceed Ratio, the load is a lower bound. - - More specifically: - - * Take all trials measured at a given load. - - * The sum of the durations of all bad full-length trials is called - the bad sum. - - * The sum of the durations of all good full-length trials is called - the good sum. - - * The result of adding the bad sum plus the good sum is called the - measured sum. - - * The larger of the measured sum and the Goal Duration Sum is called - the whole sum. - - * The whole sum minus the measured sum is called the missing sum. - - * The optimistic exceed ratio is the bad sum divided by the whole - sum. - - * The pessimistic exceed ratio is the bad sum plus the missing sum, - that divided by the whole sum. - - * If the optimistic exceed ratio is larger than the Goal Exceed - Ratio, the load is classified as an upper bound. - - * If the pessimistic exceed ratio is not larger than the Goal Exceed - Ratio, the load is classified as a lower bound. - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 41] - -Internet-Draft MLRsearch July 2024 - - - * Else, the load is classified as undecided. - - The definition of pessimistic exceed ratio is compatible with the - logic in the Conditional Throughput computation, so in this single - trial duration case, a load is a lower bound if and only if the - Conditional Throughput loss ratio is not larger than the Goal Loss - Ratio. - - If it is larger, the load is either an upper bound or undecided. - -5.4. Trials with Short Duration - -5.4.1. Scenarios - - Trials with intended duration smaller than the goal final trial - duration are called short trials. The motivation for load - classification logic in the presence of short trials is based around - a counter-factual case: What would the trial result be if a short - trial has been measured as a full-length trial instead? - - There are three main scenarios where human intuition guides the - intended behavior of load classification. - -5.4.1.1. False Good Scenario - - The user had their reason for not configuring a shorter goal final - trial duration. Perhaps SUT has buffers that may get full at longer - trial durations. Perhaps SUT shows periodic decreases in performance - the user does not want to be treated as noise. - - In any case, many good short trials may become bad full-length trials - in the counter-factual case. - - In extreme cases, there are plenty of good short trials and no bad - short trials. - - In this scenario, we want the load classification NOT to classify the - load as a lower bound, despite the abundance of good short trials. - - Effectively, we want the good short trials to be ignored, so they do - not contribute to comparisons with the Goal Duration Sum. - -5.4.1.2. True Bad Scenario - - When there is a frame loss in a short trial, the counter-factual - full-length trial is expected to lose at least as many frames. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 42] - -Internet-Draft MLRsearch July 2024 - - - In practice, bad short trials are rarely turning into good full- - length trials. - - In extreme cases, there are no good short trials. - - In this scenario, we want the load classification to classify the - load as an upper bound just based on the abundance of short bad - trials. - - Effectively, we want the bad short trials to contribute to - comparisons with the Goal Duration Sum, so the load can be classified - sooner. - -5.4.1.3. Balanced Scenario - - Some SUTs are quite indifferent to trial duration. Performance - probability function constructed from short trial results is likely - to be similar to the performance probability function constructed - from full-length trial results (perhaps with larger dispersion, but - without a big impact on the median quantiles overall). - - For a moderate Goal Exceed Ratio value, this may mean there are both - good short trials and bad short trials. - - This scenario is there just to invalidate a simple heuristic of - always ignoring good short trials and never ignoring bad short - trials, as that simple heuristic would be too biased. - - Yes, the short bad trials are likely to turn into full-length bad - trials in the counter-factual case, but there is no information on - what would the good short trials turn into. - - The only way to decide safely is to do more trials at full length, - the same as in False Good Scenario. - -5.4.2. Classification Logic - - MLRsearch picks a particular logic for load classification in the - presence of short trials, but it is still RECOMMENDED to use - configurations that imply no short trials, so the possible - inefficiencies in that logic do not affect the result, and the result - has better explainability. - - With that said, the logic differs from the single trial duration case - only in different definition of the bad sum. The good sum is still - the sum across all good full-length trials. - - Few more notions are needed for defining the new bad sum: - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 43] - -Internet-Draft MLRsearch July 2024 - - - * The sum of durations of all bad full-length trials is called the - bad long sum. - - * The sum of durations of all bad short trials is called the bad - short sum. - - * The sum of durations of all good short trials is called the good - short sum. - - * One minus the Goal Exceed Ratio is called the subceed ratio. - - * The Goal Exceed Ratio divided by the subceed ratio is called the - exceed coefficient. - - * The good short sum multiplied by the exceed coefficient is called - the balancing sum. - - * The bad short sum minus the balancing sum is called the excess - sum. - - * If the excess sum is negative, the bad sum is equal to the bad - long sum. - - * Otherwise, the bad sum is equal to the bad long sum plus the - excess sum. - - Here is how the new definition of the bad sum fares in the three - scenarios, where the load is close to what would the relevant bounds - be if only full-length trials were used for the search. - -5.4.2.1. False Good Scenario - - If the duration is too short, we expect to see a higher frequency of - good short trials. This could lead to a negative excess sum, which - has no impact, hence the load classification is given just by full- - length trials. Thus, MLRsearch using too short trials has no - detrimental effect on result comparability in this scenario. But - also using short trials does not help with overall search duration, - probably making it worse. - -5.4.2.2. True Bad Scenario - - Settings with a small exceed ratio have a small exceed coefficient, - so the impact of the good short sum is small, and the bad short sum - is almost wholly converted into excess sum, thus bad short trials - have almost as big an impact as full-length bad trials. The same - conclusion applies to moderate exceed ratio values when the good - short sum is small. Thus, short trials can cause a load to get - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 44] - -Internet-Draft MLRsearch July 2024 - - - classified as an upper bound earlier, bringing time savings (while - not affecting comparability). - -5.4.2.3. Balanced Scenario - - Here excess sum is small in absolute value, as the balancing sum is - expected to be similar to the bad short sum. Once again, full-length - trials are needed for final load classification; but usage of short - trials probably means MLRsearch needed a shorter overall search time - before selecting this load for measurement, thus bringing time - savings (while not affecting comparability). - - Note that in presence of short trial results, the comparibility - between the load classification and the Conditional Throughput is - only partial. The Conditional Throughput still comes from a good - long trial, but a load higher than the Relevant Lower Bound may also - compute to a good value. - -5.5. Trials with Longer Duration - - If there are trial results with an intended duration larger than the - goal trial duration, the precise definitions in Appendix A and - Appendix B treat them in exactly the same way as trials with duration - equal to the goal trial duration. - - But in configurations with moderate (including 0.5) or small Goal - Exceed Ratio and small Goal Loss Ratio (especially zero), bad trials - with longer than goal durations may bias the search towards the lower - load values, as the noiseful end of the spectrum gets a larger - probability of causing the loss within the longer trials. - -6. IANA Considerations - - No requests of IANA. - -7. Security Considerations - - Benchmarking activities as described in this memo are limited to - technology characterization of a DUT/SUT using controlled stimuli in - a laboratory environment, with dedicated address space and the - constraints specified in the sections above. - - The benchmarking network topology will be an independent test setup - and MUST NOT be connected to devices that may forward the test - traffic into a production network or misroute traffic to the test - management network. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 45] - -Internet-Draft MLRsearch July 2024 - - - Further, benchmarking is performed on a "black-box" basis, relying - solely on measurements observable external to the DUT/SUT. - - Special capabilities SHOULD NOT exist in the DUT/SUT specifically for - benchmarking purposes. Any implications for network security arising - from the DUT/SUT SHOULD be identical in the lab and in production - networks. - -8. Acknowledgements - - Some phrases and statements in this document were created with help - of Mistral AI (mistral.ai). - - Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough - review and numerous useful comments and suggestions in the earlier - versions of this document. - - Special wholehearted gratitude and thanks to the late Al Morton for - his thorough reviews filled with very specific feedback and - constructive guidelines. Thank you Al for the close collaboration - over the years, for your continuous unwavering encouragement full of - empathy and positive attitude. Al, you are dearly missed. - -9. Appendix A: Load Classification - - This section specifies how to perform the load classification. - - Any intended load value can be classified, according to a given - [Search Goal] (#Search-Goal). - - The algorithm uses (some subsets of) the set of all available trial - results from trials measured at a given intended load at the end of - the search. All durations are those returned by the Measurer. - - The block at the end of this appendix holds pseudocode which computes - two values, stored in variables named optimistic and pessimistic. - - The pseudocode happens to be a valid Python code. - - If values of both variables are computed to be true, the load in - question is classified as a lower bound according to the given Search - Goal. If values of both variables are false, the load is classified - as an upper bound. Otherwise, the load is classified as undecided. - - The pseudocode expects the following variables to hold values as - follows: - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 46] - -Internet-Draft MLRsearch July 2024 - - - * goal_duration_sum: The duration sum value of the given Search - Goal. - - * goal_exceed_ratio: The exceed ratio value of the given Search - Goal. - - * good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial - Loss Ratio not higher than the Goal Loss Ratio. - - * bad_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial - Loss Ratio higher than the Goal Loss Ratio. - - * good_short_sum: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a Trial Loss - Ratio not higher than the Goal Loss Ratio. - - * bad_short_sum: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a Trial Loss - Ratio higher than the Goal Loss Ratio. - - The code works correctly also when there are no trial results at a - given load. - - balancing_sum = good_short_sum * goal_exceed_ratio / (1.0 - goal_exceed_ratio) - effective_bad_sum = bad_long_sum + max(0.0, bad_short_sum - balancing_sum) - effective_whole_sum = max(good_long_sum + effective_bad_sum, goal_duration_sum) - quantile_duration_sum = effective_whole_sum * goal_exceed_ratio - optimistic = effective_bad_sum <= quantile_duration_sum - pessimistic = (effective_whole_sum - good_long_sum) <= quantile_duration_sum - -10. Appendix B: Conditional Throughput - - This section specifies how to compute Conditional Throughput, as - referred to in section [Conditional Throughput] (#Conditional- - Throughput). - - Any intended load value can be used as the basis for the following - computation, but only the Relevant Lower Bound (at the end of the - search) leads to the value called the Conditional Throughput for a - given Search Goal. - - The algorithm uses (some subsets of) the set of all available trial - results from trials measured at a given intended load at the end of - the search. All durations are those returned by the Measurer. - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 47] - -Internet-Draft MLRsearch July 2024 - - - The block at the end of this appendix holds pseudocode which computes - a value stored as variable conditional_throughput. - - The pseudocode happens to be a valid Python code. - - The pseudocode expects the following variables to hold values as - follows: - - * goal_duration_sum: The duration sum value of the given Search - Goal. - - * goal_exceed_ratio: The exceed ratio value of the given Search - Goal. - - * good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial - Loss Ratio not higher than the Goal Loss Ratio. - - * bad_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a Trial - Loss Ratio higher than the Goal Loss Ratio. - - * long_trials: An iterable of all trial results from trials with - trial duration at least equal to the goal final trial duration, - sorted by increasing the Trial Loss Ratio. A trial result is a - composite with the following two attributes available: - - - trial.loss_ratio: The Trial Loss Ratio as measured for this - trial. - - - trial.duration: The trial duration of this trial. - - The code works correctly only when there if there is at least one - trial result measured at a given load. - - all_long_sum = max(goal_duration_sum, good_long_sum + bad_long_sum) - remaining = all_long_sum * (1.0 - goal_exceed_ratio) - quantile_loss_ratio = None - for trial in long_trials: - if quantile_loss_ratio is None or remaining > 0.0: - quantile_loss_ratio = trial.loss_ratio - remaining -= trial.duration - else: - break - else: - if remaining > 0.0: - quantile_loss_ratio = 1.0 - conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 48] - -Internet-Draft MLRsearch July 2024 - - -11. References - -11.1. Normative References - - [RFC1242] Bradner, S., "Benchmarking Terminology for Network - Interconnection Devices", RFC 1242, DOI 10.17487/RFC1242, - July 1991, . - - [RFC2285] Mandeville, R., "Benchmarking Terminology for LAN - Switching Devices", RFC 2285, DOI 10.17487/RFC2285, - February 1998, . - - [RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for - Network Interconnect Devices", RFC 2544, - DOI 10.17487/RFC2544, March 1999, - . - - [RFC8219] Georgescu, M., Pislaru, L., and G. Lencse, "Benchmarking - Methodology for IPv6 Transition Technologies", RFC 8219, - DOI 10.17487/RFC8219, August 2017, - . - - [RFC9004] Morton, A., "Updates for the Back-to-Back Frame Benchmark - in RFC 2544", RFC 9004, DOI 10.17487/RFC9004, May 2021, - . - -11.2. Informative References - - [FDio-CSIT-MLRsearch] - "FD.io CSIT Test Methodology - MLRsearch", October 2023, - . - - [PyPI-MLRsearch] - "MLRsearch 1.2.1, Python Package Index", October 2023, - . - - [TST009] "TST 009", n.d., . - -Authors' Addresses - - Maciek Konstantynowicz - Cisco Systems - Email: mkonstan@cisco.com - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 49] - -Internet-Draft MLRsearch July 2024 - - - Vratko Polak - Cisco Systems - Email: vrpolak@cisco.com - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Konstantynowicz & Polak Expires 18 January 2025 [Page 50] diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.xml b/docs/ietf/draft-ietf-bmwg-mlrsearch-07.xml deleted file mode 100644 index c3aede3d3b..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-07.xml +++ /dev/null @@ -1,3136 +0,0 @@ - - - - - - - - - - - - - - - -]> - - - - - Multiple Loss Ratio Search - - - Cisco Systems -
- mkonstan@cisco.com -
- - - Cisco Systems -
- vrpolak@cisco.com -
- - - - - ops - Benchmarking Working Group - Internet-Draft - - - - - - -This document proposes extensions to throughput search by -defining a new methodology called Multiple Loss Ratio search -(MLRsearch). MLRsearch aims to minimize search duration, -support multiple loss ratio searches, -and enhance result repeatability and comparability. - -The primary reason for extending is to address the challenges -and requirements presented by the evaluation and testing -of software-based networking systems' data planes. - -To give users more freedom, MLRsearch provides additional configuration options -such as allowing multiple short trials per load instead of one large trial, -tolerating a certain percentage of trial results with higher loss, -and supporting the search for multiple goals with varying loss ratios. - - - - - - - - - - - - - - - -
Purpose and Scope - -The purpose of this document is to describe Multiple Loss Ratio search -(MLRsearch), a data plane throughput search methodology optimized for software -networking DUTs. - -Applying vanilla throughput bisection to software DUTs -results in several problems: - - - Binary search takes too long as most trials are done far from the -eventually found throughput. - The required final trial duration and pauses between trials -prolong the overall search duration. - Software DUTs show noisy trial results, -leading to a big spread of possible discovered throughput values. - Throughput requires a loss of exactly zero frames, but the industry -frequently allows for small but non-zero losses. - The definition of throughput is not clear when trial results are inconsistent. - - -To address the problems mentioned above, -the MLRsearch test methodology specification employs the following enhancements: - - - Allow multiple short trials instead of one big trial per load. - - Optionally, tolerate a percentage of trial results with higher loss. - - Allow searching for multiple Search Goals, with differing loss ratios. - - Any trial result can affect each Search Goal in principle. - - Insert multiple coarse targets for each Search Goal, earlier ones need -to spend less time on trials. - - Earlier targets also aim for lesser precision. - Use Forwarding Rate (FR) at maximum offered load - (section 3.6.2) to initialize the initial targets. - - Take care when dealing with inconsistent trial results. - - Reported throughput is smaller than the smallest load with high loss. - Smaller load candidates are measured first. - - Apply several load selection heuristics to save even more time -by trying hard to avoid unnecessarily narrow bounds. - - -Some of these enhancements are formalized as MLRsearch specification, -the remaining enhancements are treated as implementation details, -thus achieving high comparability without limiting future improvements. - -MLRsearch configuration options are flexible enough to -support both conservative settings and aggressive settings. -The conservative settings lead to results -unconditionally compliant with , -but longer search duration and worse repeatability. -Conversely, aggressive settings lead to shorter search duration -and better repeatability, but the results are not compliant with . - -No part of is intended to be obsoleted by this document. - -
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Identified Problems - -This chapter describes the problems affecting usability -of various performance testing methodologies, -mainly a binary search for unconditionally compliant throughput. - -
Long Search Duration - - -The emergence of software DUTs, with frequent software updates and a -number of different frame processing modes and configurations, -has increased both the number of performance tests -required to verify the DUT update and the frequency of running those tests. -This makes the overall test execution time even more important than before. - -The current throughput definition restricts the potential -for time-efficiency improvements. -A more generalized throughput concept could enable further enhancements -while maintaining the precision of simpler methods. - -The bisection method, when unconditionally compliant with , -is excessively slow. -This is because a significant amount of time is spent on trials -with loads that, in retrospect, are far from the final determined throughput. - - does not specify any stopping condition for throughput search, -so users already have an access to a limited trade-off -between search duration and achieved precision. -However, each full 60-second trials doubles the precision, -so not many trials can be removed without a substantial loss of precision. - -
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DUT in SUT - - defines: -- DUT as - - The network forwarding device to which stimulus is offered and - response measured (section 3.1.1). -- SUT as - - The collective set of network devices to which stimulus is offered - as a single entity and response measured (section 3.1.2). - - specifies a test setup with an external tester stimulating the -networking system, treating it either as a single DUT, or as a system -of devices, an SUT. - -In the case of software networking, the SUT consists of not only the DUT -as a software program processing frames, but also of -server hardware and operating system functions, -with that server hardware resources shared across all programs including -the operating system. - -Given that the SUT is a shared multi-tenant environment -encompassing the DUT and other components, the DUT might inadvertently -experience interference from the operating system -or other software operating on the same server. - -Some of this interference can be mitigated. -For instance, -pinning DUT program threads to specific CPU cores -and isolating those cores can prevent context switching. - -Despite taking all feasible precautions, some adverse effects may still impact -the DUT's network performance. -In this document, these effects are collectively -referred to as SUT noise, even if the effects are not as unpredictable -as what other engineering disciplines call noise. - -DUT can also exhibit fluctuating performance itself, for reasons -not related to the rest of SUT. For example due to pauses in execution -as needed for internal stateful processing. -In many cases this -may be an expected per-design behavior, as it would be observable even -in a hypothetical scenario where all sources of SUT noise are eliminated. -Such behavior affects trial results in a way similar to SUT noise. -As the two phenomenons are hard to distinguish, -in this document the term 'noise' is used to encompass -both the internal performance fluctuations of the DUT -and the genuine noise of the SUT. - -A simple model of SUT performance consists of an idealized noiseless performance, -and additional noise effects. -For a specific SUT, the noiseless performance is assumed to be constant, -with all observed performance variations being attributed to noise. -The impact of the noise can vary in time, sometimes wildly, -even within a single trial. -The noise can sometimes be negligible, but frequently -it lowers the observed SUT performance as observed in trial results. - -In this model, SUT does not have a single performance value, it has a spectrum. -One end of the spectrum is the idealized noiseless performance value, -the other end can be called a noiseful performance. -In practice, trial result -close to the noiseful end of the spectrum happens only rarely. -The worse the performance value is, the more rarely it is seen in a trial. -Therefore, the extreme noiseful end of the SUT spectrum is not observable -among trial results. -Also, the extreme noiseless end of the SUT spectrum -is unlikely to be observable, this time because some small noise effects -are likely to occur multiple times during a trial. - -Unless specified otherwise, this document's focus is -on the potentially observable ends of the SUT performance spectrum, -as opposed to the extreme ones. - -When focusing on the DUT, the benchmarking effort should ideally aim -to eliminate only the SUT noise from SUT measurements. -However, -this is currently not feasible in practice, as there are no realistic enough -models available to distinguish SUT noise from DUT fluctuations, -based on authors' experience and available literature. - -Assuming a well-constructed SUT, the DUT is likely its -primary performance bottleneck. -In this case, we can define the DUT's -ideal noiseless performance as the noiseless end of the SUT performance spectrum, -especially for throughput. -However, other performance metrics, such as latency, -may require additional considerations. - -Note that by this definition, DUT noiseless performance -also minimizes the impact of DUT fluctuations, as much as realistically possible -for a given trial duration. - -MLRsearch methodology aims to solve the DUT in SUT problem -by estimating the noiseless end of the SUT performance spectrum -using a limited number of trial results. - -Any improvements to the throughput search algorithm, aimed at better -dealing with software networking SUT and DUT setup, should employ -strategies recognizing the presence of SUT noise, allowing the discovery of -(proxies for) DUT noiseless performance -at different levels of sensitivity to SUT noise. - -
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Repeatability and Comparability - - does not suggest to repeat throughput search. -And from just one -discovered throughput value, it cannot be determined how repeatable that value is. -Poor repeatability then leads to poor comparability, -as different benchmarking teams may obtain varying throughput values -for the same SUT, exceeding the expected differences from search precision. - - throughput requirements (60 seconds trial and -no tolerance of a single frame loss) affect the throughput results -in the following way. -The SUT behavior close to the noiseful end of its performance spectrum -consists of rare occasions of significantly low performance, -but the long trial duration makes those occasions not so rare on the trial level. -Therefore, the binary search results tend to wander away from the noiseless end -of SUT performance spectrum, more frequently and more widely than short -trials would, thus causing poor throughput repeatability. - -The repeatability problem can be addressed by defining a search procedure -that identifies a consistent level of performance, -even if it does not meet the strict definition of throughput in . - -According to the SUT performance spectrum model, better repeatability -will be at the noiseless end of the spectrum. -Therefore, solutions to the DUT in SUT problem -will help also with the repeatability problem. - -Conversely, any alteration to throughput search -that improves repeatability should be considered -as less dependent on the SUT noise. - -An alternative option is to simply run a search multiple times, and report some -statistics (e.g. average and standard deviation). -This can be used -for a subset of tests deemed more important, -but it makes the search duration problem even more pronounced. - -
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Throughput with Non-Zero Loss - - (section 3.17 Throughput) defines throughput as: - The maximum rate at which none of the offered frames - are dropped by the device. - -Then, it says: - Since even the loss of one frame in a - data stream can cause significant delays while - waiting for the higher level protocols to time out, - it is useful to know the actual maximum data - rate that the device can support. - -However, many benchmarking teams accept a small, -non-zero loss ratio as the goal for their load search. - -Motivations are many: - - - Modern protocols tolerate frame loss better, -compared to the time when and were specified. - Trials nowadays send way more frames within the same duration, -increasing the chance of a small SUT performance fluctuation -being enough to cause frame loss. - Small bursts of frame loss caused by noise have otherwise smaller impact -on the average frame loss ratio observed in the trial, -as during other parts of the same trial the SUT may work more closely -to its noiseless performance, thus perhaps lowering the Trial Loss Ratio -below the Goal Loss Ratio value. - If an approximation of the SUT noise impact on the Trial Loss Ratio is known, -it can be set as the Goal Loss Ratio. - - -Regardless of the validity of all similar motivations, -support for non-zero loss goals makes any search algorithm more user-friendly. - throughput is not user-friendly in this regard. - -Furthermore, allowing users to specify multiple loss ratio values, -and enabling a single search to find all relevant bounds, -significantly enhances the usefulness of the search algorithm. - -Searching for multiple Search Goals also helps to describe the SUT performance -spectrum better than the result of a single Search Goal. -For example, the repeated wide gap between zero and non-zero loss loads -indicates the noise has a large impact on the observed performance, -which is not evident from a single goal load search procedure result. - -It is easy to modify the vanilla bisection to find a lower bound -for the intended load that satisfies a non-zero Goal Loss Ratio. -But it is not that obvious how to search for multiple goals at once, -hence the support for multiple Search Goals remains a problem. - -
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Inconsistent Trial Results - -While performing throughput search by executing a sequence of -measurement trials, there is a risk of encountering inconsistencies -between trial results. - -The plain bisection never encounters inconsistent trials. -But hints about the possibility of inconsistent trial results, -in two places in its text. -The first place is section 24, where full trial durations are required, -presumably because they can be inconsistent with the results -from short trial durations. -The second place is section 26.3, where two successive zero-loss trials -are recommended, presumably because after one zero-loss trial -there can be a subsequent inconsistent non-zero-loss trial. - -Examples include: - - - A trial at the same load (same or different trial duration) results -in a different Trial Loss Ratio. - A trial at a higher load (same or different trial duration) results -in a smaller Trial Loss Ratio. - - -Any robust throughput search algorithm needs to decide how to continue -the search in the presence of such inconsistencies. -Definitions of throughput in and are not specific enough -to imply a unique way of handling such inconsistencies. - -Ideally, there will be a definition of a new quantity which both generalizes -throughput for non-zero-loss (and other possible repeatability enhancements), -while being precise enough to force a specific way to resolve trial result -inconsistencies. -But until such a definition is agreed upon, the correct way to handle -inconsistent trial results remains an open problem. - -
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MLRsearch Specification - -This section describes MLRsearch specification including all technical -definitions needed for evaluating whether a particular test procedure -complies with MLRsearch specification. - - -
Overview - -MLRsearch specification describes a set of abstract system components, -acting as functions with specified inputs and outputs. - -A test procedure is said to comply with MLRsearch specification -if it can be conceptually divided into analogous components, -each satisfying requirements for the corresponding MLRsearch component. - -The Measurer component is tasked to perform trials, -the Controller component is tasked to select trial loads and durations, -the Manager component is tasked to pre-configure everything -and to produce the test report. -The test report explicitly states Search Goals (as the Controller Inputs) -and corresponding Goal Results (Controller Outputs). - - -The Manager calls the Controller once, -the Controller keeps calling the Measurer -until all stopping conditions are met. - -The part where Controller calls the Measurer is called the search. -Any activity done by the Manager before it calls the Controller -(or after Controller returns) is not considered to be part of the search. - -MLRsearch specification prescribes regular search results and recommends -their stopping conditions. Irregular search results are also allowed, -they may have different requirements and stopping conditions. - -Search results are based on load classification. -When measured enough, any chosen load either achieves of fails each search goal, -thus becoming a lower or an upper bound for that goal. -When the relevant bounds are at loads that are close enough -(according to goal precision), the regular result is found. -Search stops when all regular results are found -(or if some goals are proven to have only irregular results). - -
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Measurement Quantities - -MLRsearch specification uses a number of measurement quantities. - -In general, MLRsearch specification does not require particular units to be used, -but it is REQUIRED for the test report to state all the units. -For example, ratio quantities can be dimensionless numbers between zero and one, -but may be expressed as percentages instead. - -For convenience, a group of quantities can be treated as a composite quantity, -One constituent of a composite quantity is called an attribute, -and a group of attribute values is called an instance of that composite quantity. - -Some attributes are not independent from others, -and they can be calculated from other attributes. -Such quantites are called derived quantities. - -
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Existing Terms - -RFC 1242 "Benchmarking Terminology for Network Interconnect Devices" -contains basic definitions, and -RFC 2544 "Benchmarking Methodology for Network Interconnect Devices" -contains discussions of a number of terms and additional methodology requirements. -RFC 2285 adds more terms and discussions, describing some known situations -in more precise way. - -All three documents should be consulted -before attempting to make use of this document. - -Definitions of some central terms are copied and discussed in subsections. - - - - - -
SUT - -Defined in (section 3.1.2 System Under Test (SUT)) as follows. - -Definition: - -The collective set of network devices to which stimulus is offered -as a single entity and response measured. - -Discussion: - -An SUT consisting of a single network device is also allowed. - -
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DUT - -Defined in (section 3.1.1 Device Under Test (DUT)) as follows. - -Definition: - -The network forwarding device to which stimulus is offered and -response measured. - -Discussion: - -DUT, as a sub-component of SUT, is only indirectly mentioned -in MLRsearch specification, but is of key relevance for its motivation. - - -
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Trial - -A trial is the part of the test described in (section 23. Trial description). - -Definition: - -A particular test consists of multiple trials. Each trial returns - one piece of information, for example the loss rate at a particular - input frame rate. Each trial consists of a number of phases: - -a) If the DUT is a router, send the routing update to the "input" - port and pause two seconds to be sure that the routing has settled. - -b) Send the "learning frames" to the "output" port and wait 2 - seconds to be sure that the learning has settled. Bridge learning - frames are frames with source addresses that are the same as the - destination addresses used by the test frames. Learning frames for - other protocols are used to prime the address resolution tables in - the DUT. The formats of the learning frame that should be used are - shown in the Test Frame Formats document. - -c) Run the test trial. - -d) Wait for two seconds for any residual frames to be received. - -e) Wait for at least five seconds for the DUT to restabilize. - -Discussion: - -The definition describes some traits, it is not clear whether all of them -are REQUIRED, or some of them are only RECOMMENDED. - - -For the purposes of the MLRsearch specification, -it is ALLOWED for the test procedure to deviate from the description, -but any such deviation MUST be made explicit in the test report. - -Trials are the only stimuli the SUT is expected to experience -during the search. - -In some discussion paragraphs, it is useful to consider the traffic -as sent and received by a tester, as implicitly defined -in (section 6. Test set up). - -An example of deviation from is using shorter wait times. - -
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Trial Terms - -This section defines new and redefine existing terms for quantities -relevant as inputs or outputs of trial, as used by the Measurer component. - -
Trial Duration - -Definition: - -Trial duration is the intended duration of the traffic for a trial. - -Discussion: - -In general, this quantity does not include any preparation nor waiting -described in section 23 of (section 23. Trial description). - -While any positive real value may be provided, some Measurer implementations -MAY limit possible values, e.g. by rounding down to neared integer in seconds. -In that case, it is RECOMMENDED to give such inputs to the Controller -so the Controller only proposes the accepted values. -Alternatively, the test report MUST present the rounded values -as Search Goal attributes. - -
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Trial Load - -Definition: - -The trial load is the intended load for a trial - -Discussion: - -For test report purposes, it is assumed that this is a constant load by default. -This MAY be only an average load, e.g. when the traffic is intended to be busty, -e.g. as suggested in (section 21. Bursty traffic), -but the test report MUST explicitly mention how non-constant the traffic is. - -Trial load is the quantity defined as Constant Load of -(section 3.4 Constant Load), Data Rate of -(section 14. Bidirectional traffic) -and Intended Load of (section 3.5.1 Intended load (Iload)). -All three definitions specify -that this value applies to one (input or output) interface. - - -For test report purposes, multi-interface aggregate load MAY be reported, -this is understood as the same quantity expressed using different units. -From the report it MUST be clear whether a particular trial load value -is per one interface, or an aggregate over all interfaces. - -Similarly to trial duration, some Measurers may limit the possible values -of trial load. Contrary to trial duration, the test report is NOT REQUIRED -to document such behavior. - - -It is ALLOWED to combine trial load and trial duration in a way -that would not be possible to achieve using any integer number of data frames. - - -
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Trial Input - -Definition: - -Trial Input is a composite quantity, consisting of two attributes: -trial duration and trial load. - -Discussion: - -When talking about multiple trials, it is common to say "Trial Inputs" -to denote all corresponding Trial Input instances. - -A Trial Input instance acts as the input for one call of the Measurer component. - -Contrary to other composite quantities, MLRsearch implementations -are NOT ALLOWED to add optional attributes here. -This improves interoperability between various implementations of -the Controller and the Measurer. - -
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Traffic Profile - -Definition: - -Traffic profile is a composite quantity -containing attributes other than trial load and trial duration, -needed for unique determination of the trial to be performed. - -Discussion: - -All its attributes are assumed to be constant during the search, -and the composite is configured on the Measurer by the Manager -before the search starts. -This is why the traffic profile is not part of the Trial Input. - -As a consequence, implementations of the Manager and the Measurer -must be aware of their common set of capabilities, so that the traffic profile -uniquely defines the traffic during the search. -The important fact is that none of those capabilities -have to be known by the Controller implementations. - -The traffic profile SHOULD contain some specific quantities, -for example (section 9. Frame sizes) governs -data link frame size as defined in (section 3.5 Data link frame size). - -Several more specific quantities may be RECOMMENDED, depending on media type. -For example, (Appendix C) lists frame formats and protocol addresses, -as recommended from (section 8. Frame formats) -and (section 12. Protocol addresses). - -Depending on SUT configuration, e.g. when testing specific protocols, -additional attributes MUST be included in the traffic profile -and in the test report. - -Example: (section 5.3. Traffic Setup) introduces traffic setups -consisting of a mix of IPv4 and IPv6 traffic - the implied traffic profile -therefore must include an attribute for their percentage. - -Other traffic properties that need to be somehow specified -in Traffic Profile include: - (section 14. Bidirectional traffic), - (section 3.3.3 Fully meshed traffic), -and (section 11. Modifiers). - -
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Trial Forwarding Ratio - -Definition: - -The trial forwarding ratio is a dimensionless floating point value. -It MUST range between 0.0 and 1.0, both inclusive. -It is calculated by dividing the number of frames -successfully forwarded by the SUT -by the total number of frames expected to be forwarded during the trial - -Discussion: - -For most traffic profiles, "expected to be forwarded" means -"intended to get transmitted from Tester towards SUT". - -Trial forwarding ratio MAY be expressed in other units -(e.g. as a percentage) in the test report. - -Note that, contrary to loads, frame counts used to compute -trial forwarding ratio are aggregates over all SUT output interfaces. - -Questions around what is the correct number of frames -that should have been forwarded -is generally outside of the scope of this document. - - - -
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Trial Loss Ratio - -Definition: - -The Trial Loss Ratio is equal to one minus the trial forwarding ratio. - -Discussion: - -100% minus the trial forwarding ratio, when expressed as a percentage. - -This is almost identical to Frame Loss Rate of -(section 3.6 Frame Loss Rate), -the only minor difference is that Trial Loss Ratio -does not need to be expressed as a percentage. - -
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Trial Forwarding Rate - -Definition: - -The trial forwarding rate is a derived quantity, calculated by -multiplying the trial load by the trial forwarding ratio. - -Discussion: - -It is important to note that while similar, this quantity is not identical -to the Forwarding Rate as defined in -(section 3.6.1 Forwarding rate (FR)). -The latter is specific to one output interface only, -whereas the trial forwarding ratio is based -on frame counts aggregated over all SUT output interfaces. - - -
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Trial Effective Duration - -Definition: - -Trial effective duration is a time quantity related to the trial, -by default equal to the trial duration. - -Discussion: - -This is an optional feature. -If the Measurer does not return any trial effective duration value, -the Controller MUST use the trial duration value instead. - -Trial effective duration may be any time quantity chosen by the Measurer -to be used for time-based decisions in the Controller. - -The test report MUST explain how the Measurer computes the returned -trial effective duration values, if they are not always -equal to the trial duration. - -This feature can be beneficial for users -who wish to manage the overall search duration, -rather than solely the traffic portion of it. -Simply measure the duration of the whole trial (waits including) -and use that as the trial effective duration. - -Also, this is a way for the Measurer to inform the Controller about -its surprising behavior, for example when rounding the trial duration value. - - -
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Trial Output - -Definition: - -Trial Output is a composite quantity. The REQUIRED attributes are -Trial Loss Ratio, trial effective duration and trial forwarding rate. - -Discussion: - -When talking about multiple trials, it is common to say "Trial Outputs" -to denote all corresponding Trial Output instances. - -Implementations may provide additional (optional) attributes. -The Controller implementations MUST ignore values of any optional attribute -they are not familiar with, -except when passing Trial Output instance to the Manager. - -Example of an optional attribute: -The aggregate number of frames expected to be forwarded during the trial, -especially if it is not just (a rounded-up value) -implied by trial load and trial duration. - -While (Section 3.5.2 Offered load (Oload)) -requires the offered load value to be reported for forwarding rate measurements, -it is NOT REQUIRED in MLRsearch specification. - - -
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Trial Result - -Definition: - -Trial result is a composite quantity, -consisting of the Trial Input and the Trial Output. - -Discussion: - -When talking about multiple trials, it is common to say "trial results" -to denote all corresponding trial result instances. - -While implementations SHOULD NOT include additional attributes -with independent values, they MAY include derived quantities. - -
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Goal Terms - -This section defines new and redefine existing terms for quantities -indirectly relevant for inputs or outputs of the Controller component. - -Several goal attributes are defined before introducing -the main component quantity: the Search Goal. - -
Goal Final Trial Duration - -Definition: - -A threshold value for trial durations. - -Discussion: - -This attribute value MUST be positive. - -A trial with Trial Duration at least as long as the Goal Final Trial Duration -is called a full-length trial (with respect to the given Search Goal). - -A trial that is not full-length is called a short trial. - -Informally, while MLRsearch is allowed to perform short trials, -the results from such short trials have only limited impact on search results. - -One trial may be full-length for some Search Goals, but not for others. - -The full relation of this goal to Controller Output is defined later in -this document in subsections of [Goal Result] (#Goal-Result). -For example, the Conditional Throughput for this goal is computed only from -full-length trial results. - -
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Goal Duration Sum - -Definition: - -A threshold value for a particular sum of trial effective durations. - -Discussion: - -This attribute value MUST be positive. - -Informally, even when looking only at full-length trials, -MLRsearch may spend up to this time measuring the same load value. - -If the Goal Duration Sum is larger than the Goal Final Trial Duration, -multiple full-length trials may need to be performed at the same load. - -See [TST009 Example] (#TST009-Example) for an example where possibility -of multiple full-length trials at the same load is intended. - -A Goal Duration Sum value lower than the Goal Final Trial Duration -(of the same goal) could save some search time, but is NOT RECOMMENDED. -See [Relevant Upper Bound] (#Relevant-Upper-Bound) for partial explanation. - -
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Goal Loss Ratio - -Definition: - -A threshold value for Trial Loss Ratios. - -Discussion: - -Attribute value MUST be non-negative and smaller than one. - -A trial with Trial Loss Ratio larger than a Goal Loss Ratio value -is called a lossy trial, with respect to given Search Goal. - -Informally, if a load causes too many lossy trials, -the Relevant Lower Bound for this goal will be smaller than that load. - -If a trial is not lossy, it is called a low-loss trial, -or (specifically for zero Goal Loss Ratio value) zero-loss trial. - -
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Goal Exceed Ratio - -Definition: - -A threshold value for a particular ratio of sums of Trial Effective Durations. - -Discussion: - -Attribute value MUST be non-negative and smaller than one. - -See later sections for details on which sums. -Specifically, the direct usage is only in -[Appendix A: Load Classification] (#Appendix-A:-Load-Classification) -and [Appendix B: Conditional Throughput] (#Appendix-B:-Conditional-Throughput). -The impact of that usage is discussed in subsections leading to -[Goal Result] (#Goal-Result). - -Informally, the impact of lossy trials is controlled by this value. -Effectively, Goal Exceed Ratio is a percentage of full-length trials -that may be lossy without the load being classified -as the [Relevant Upper Bound] (#Relevant-Upper-Bound). - -
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Goal Width - -Definition: - -A value used as a threshold for deciding -whether two trial load values are close enough. - -Discussion: - -If present, the value MUST be positive. - -Informally, this acts as a stopping condition, -controlling the precision of the search. -The search stops if every goal has reached its precision. - -Implementations without this attribute -MUST give the Controller other ways to control the search stopping conditions. - -Absolute load difference and relative load difference are two popular choices, -but implementations may choose a different way to specify width. - -The test report MUST make it clear what specific quantity is used as Goal Width. - -It is RECOMMENDED to set the Goal Width (as relative difference) value -to a value no smaller than the Goal Loss Ratio. -(The reason is not obvious, see [Throughput] (#Throughput) if interested.) - -
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Search Goal - -Definition: - -The Search Goal is a composite quantity consisting of several attributes, -some of them are required. - -Required attributes: -- Goal Final Trial Duration -- Goal Duration Sum -- Goal Loss Ratio -- Goal Exceed Ratio - -Optional attribute: -- Goal Width - -Discussion: - -Implementations MAY add their own attributes. -Those additional attributes may be required by the implementation -even if they are not required by MLRsearch specification. -But it is RECOMMENDED for those implementations -to support missing values by computing reasonable defaults. - -The meaning of listed attributes is formally given only by their indirect effect -on the search results. - -Informally, later sections provide additional intuitions and examples -of the Search Goal attribute values. - -An example of additional attributes required by some implementations -is Goal Initial Trial Duration, together with another attribute -that controls possible intermediate Trial Duration values. -The reasonable default in this case is using the Goal Final Trial Duration -and no intermediate values. - -
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Controller Input - -Definition: - -Controller Input is a composite quantity -required as an input for the Controller. -The only REQUIRED attribute is a list of Search Goal instances. - -Discussion: - -MLRsearch implementations MAY use additional attributes. -Those additional attributes may be required by the implementation -even if they are not required by MLRsearch specification. - -Formally, the Manager does not apply any Controller configuration -apart from one Controller Input instance. - -For example, Traffic Profile is configured on the Measurer by the Manager -(without explicit assistance of the Controller). - -The order of Search Goal instances in a list SHOULD NOT -have a big impact on Controller Output (see section [Controller Output] (#Controller-Output) , -but MLRsearch implementations MAY base their behavior on the order -of Search Goal instances in a list. - -An example of an optional attribute (outside the list of Search Goals) -required by some implementations is Max Load. -While this is a frequently used configuration parameter, -already governed by (section 20. Maximum frame rate) -and (3.5.3 Maximum offered load (MOL)), -some implementations may detect or discover it instead. - - - -In MLRsearch specification, the [Relevant Upper Bound] (#Relevant-Upper-Bound) -is added as a required attribute precisely because it makes the search result -independent of Max Load value. - - -
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Search Goal Examples - -
RFC2544 Goal - -The following set of values makes the search result unconditionally compliant -with (section 24 Trial duration) - - - Goal Final Trial Duration = 60 seconds - Goal Duration Sum = 60 seconds - Goal Loss Ratio = 0% - Goal Exceed Ratio = 0% - - -The latter two attributes are enough to make the search goal -conditionally compliant, adding the first attribute -makes it unconditionally compliant. - -The second attribute (Goal Duration Sum) only prevents MLRsearch -from repeating zero-loss full-length trials. - -Non-zero exceed ratio could prolong the search and allow loss inversion -between lower-load lossy short trial and higher-load full-length zero-loss trial. -From alone, it is not clear whether that higher load -could be considered as compliant throughput. - -
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TST009 Goal - -One of the alternatives to RFC2544 is described in - (section 12.3.3 Binary search with loss verification). -The idea there is to repeat lossy trials, hoping for zero loss on second try, -so the results are closer to the noiseless end of performance sprectum, -and more repeatable and comparable. - -Only the variant with "z = infinity" is achievable with MLRsearch. - - -For example, for "r = 2" variant, the following search goal should be used: - - - Goal Final Trial Duration = 60 seconds - Goal Duration Sum = 120 seconds - Goal Loss Ratio = 0% - Goal Exceed Ratio = 50% - - -If the first 60s trial has zero loss, it is enough for MLRsearch to stop -measuring at that load, as even a second lossy trial -would still fit within the exceed ratio. - -But if the first trial is lossy, MLRsearch needs to perform also -the second trial to classify that load. -As Goal Duration Sum is twice as long as Goal Final Trial Duration, -third full-length trial is never needed. - -
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Result Terms - -Before defining the output of the Controller, -it is useful to define what the Goal Result is. - -The Goal Result is a composite quantity. - -Following subsections define its attribute first, before describing the Goal Result quantity. - -There is a correspondence between Search Goals and Goal Results. -Most of the following subsections refer to a given Search Goal, -when defining attributes of the Goal Result. -Conversely, at the end of the search, each Search Goal -has its corresponding Goal Result. - -Conceptually, the search can be seen as a process of load classification, -where the Controller attempts to classify some loads as an Upper Bound -or a Lower Bound with respect to some Search Goal. - -Before defining real attributes of the goal result, -it is useful to define bounds in general. - -
Relevant Upper Bound - -Definition: - -The Relevant Upper Bound is the smallest trial load value that is classified -at the end of the search as an upper bound -(see [Appendix A: Load Classification] (#Appendix-A:-Load-Classification)) -for the given Search Goal. - -Discussion: - -One search goal can have many different load classified as an upper bound. -At the end of the search, one of those loads will be the smallest, -becoming the relevant upper bound for that goal. - -In more detail, the set of all trial outputs (both short and full-length, -enough of them according to Goal Duration Sum) -performed at that smallest load failed to uphold all the requirements -of the given Search Goal, mainly the Goal Loss Ratio -in combination with the Goal Exceed Ratio. - - -If Max Load does not cause enough lossy trials, -the Relevant Upper Bound does not exist. -Conversely, if Relevant Upper Bound exists, -it is not affected by Max Load value. - - - -
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Relevant Lower Bound - -Definition: - -The Relevant Lower Bound is the largest trial load value -among those smaller than the Relevant Upper Bound, -that got classified at the end of the search as a lower bound (see -[Appendix A: Load Classification] (#Appendix-A:-Load-Classification)) -for the given Search Goal. - -Discussion: - -Only among loads smaller that the relevant upper bound, -the largest load becomes the relevant lower bound. -With loss inversion, stricter upper bound matters. - -In more detail, the set of all trial outputs (both short and full-length, -enough of them according to Goal Duration Sum) -performed at that largest load managed to uphold all the requirements -of the given Search Goal, mainly the Goal Loss Ratio -in combination with the Goal Exceed Ratio. - -Is no load had enough low-loss trials, the relevant lower bound -MAY not exist. - - -Strictly speaking, if the Relevant Upper Bound does not exist, -the Relevant Lower Bound also does not exist. -In that case, Max Load is classified as a lower bound, -but it is not clear whether a higher lower bound -would be found if the search used a higher Max Load value. - -For a regular Goal Result, the distance between the Relevant Lower Bound -and the Relevant Upper Bound MUST NOT be larger than the Goal Width, -if the implementation offers width as a goal attribute. - - -Searching for anther search goal may cause a loss inversion phenomenon, -where a lower load is classified as an upper bound, -but also a higher load is classified as a lower bound for the same search goal. -The definition of the Relevant Lower Bound ignores such high lower bounds. - - -
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Conditional Throughput - -Definition: - -The Conditional Throughput (see section [Appendix B: Conditional Throughput] (#Appendix-B:-Conditional-Throughput)) -as evaluated at the Relevant Lower Bound of the given Search Goal -at the end of the search. - -Discussion: - -Informally, this is a typical trial forwarding rate, expected to be seen -at the Relevant Lower Bound of the given Search Goal. - -But frequently it is only a conservative estimate thereof, -as MLRsearch implementations tend to stop gathering more data -as soon as they confirm the value cannot get worse than this estimate -within the Goal Duration Sum. - -This value is RECOMMENDED to be used when evaluating repeatability -and comparability if different MLRsearch implementations. - - -
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Goal Result - -Definition: - -The Goal Result is a composite quantity consisting of several attributes. -Relevant Upper Bound and Relevant Lower Bound are REQUIRED attributes, -Conditional Throughput is a RECOMMENDED attribute. - -Discussion: - -Depending on SUT behavior, it is possible that one or both relevant bounds -do not exist. The goal result instance where the required attribute values exist -is informally called a Regular Goal Result instance, -so we can say some goals reached Irregular Goal Results. - - -A typical Irregular Goal Result is when all trials at the Max Load -have zero loss, as the Relevant Upper Bound does not exist in that case. - -It is RECOMMENDED that the test report will display such results appropriately, -although MLRsearch specification does not prescibe how. - - -Anything else regarging Irregular Goal Results, -including their role in stopping conditions of the search -is outside the scope of this document. - -
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Search Result - -Definition: - -The Search Result is a single composite object -that maps each Search Goal instance to a corresponding Goal Result instance. - -Discussion: - -Alternatively, the Search Result can be implemented as an ordered list -of the Goal Result instances, matching the order of Search Goal instances. - - -The Search Result (as a mapping) -MUST map from all the Search Goal instances present in the Controller Input. - - - -
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Controller Output - -Definition: - -The Controller Output is a composite quantity returned from the Controller -to the Manager at the end of the search. -The Search Result instance is its only REQUIRED attribute. - -Discussion: - -MLRsearch implementation MAY return additional data in the Controller Output. - - -
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MLRsearch Architecture - - -MLRsearch architecture consists of three main system components: -the Manager, the Controller, and the Measurer. - -The architecture also implies the presence of other components, -such as the SUT and the Tester (as a sub-component of the Measurer). - -Protocols of communication between components are generally left unspecified. -For example, when MLRsearch specification mentions "Controller calls Measurer", -it is possible that the Controller notifies the Manager -to call the Measurer indirectly instead. This way the Measurer implementations -can be fully independent from the Controller implementations, -e.g. programmed in different programming languages. - -
Measurer - -Definition: - -The Measurer is an abstract system component -that when called with a [Trial Input] (#Trial-Input) instance, -performs one [Trial] (#Trial), -and returns a [Trial Output] (#Trial-Output) instance. - -Discussion: - -This definition assumes the Measurer is already initialized. -In practice, there may be additional steps before the search, -e.g. when the Manager configures the traffic profile -(either on the Measurer or on its tester sub-component directly) -and performs a warmup (if the tester requires one). - -It is the responsibility of the Measurer implementation to uphold -any requirements and assumptions present in MLRsearch specification, -e.g. trial forwarding ratio not being larger than one. - -Implementers have some freedom. -For example (section 10. Verifying received frames) -gives some suggestions (but not requirements) related to -duplicated or reordered frames. -Implementations are RECOMMENDED to document their behavior -related to such freedoms in as detailed a way as possible. - -It is RECOMMENDED to benchmark the test equipment first, -e.g. connect sender and receiver directly (without any SUT in the path), -find a load value that guarantees the offered load is not too far -from the intended load, and use that value as the Max Load value. -When testing the real SUT, it is RECOMMENDED to turn any big difference -between the intended load and the offered load into increased Trial Loss Ratio. - -Neither of the two recommendations are made into requirements, -because it is not easy to tell when the difference is big enough, -in a way thay would be dis-entangled from other Measurer freedoms. - -
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Controller - -Definition: - -The Controller is an abstract system component -that when called with a Controller Input instance -repeatedly computes Trial Input instance for the Measurer, -obtains corresponding Trial Output instances, -and eventually returns a Controller Output instance. - -Discussion: - -Informally, the Controller has big freedom in selection of Trial Inputs, -and the implementations want to achieve the Search Goals -in the shortest expected time. - -The Controller's role in optimizing the overall search time -distinguishes MLRsearch algorithms from simpler search procedures. - -Informally, each implementation can have different stopping conditions. -Goal Width is only one example. -In practice, implementation details do not matter, -as long as Goal Results are regular. - -
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Manager - -Definition: - -The Manager is an abstract system component that is reponsible for -configuring other components, calling the Controller component once, -and for creating the test report following the reporting format as -defined in (section 26. Benchmarking tests). - -Discussion: - -The Manager initializes the SUT, the Measurer (and the Tester if independent) -with their intended configurations before calling the Controller. - -The Manager does not need to be able to tweak any Search Goal attributes, -but it MUST report all applied attribute values even if not tweaked. - - -In principle, there should be a "user" (human or CI) -that "starts" or "calls" the Manager and receives the report. -The Manager MAY be able to be called more than once whis way. - - -
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Implementation Compliance - -Any networking measurement setup where there can be logically delineated system components -and there are components satisfying requirements for the Measurer, -the Controller and the Manager, is considered to be compliant with MLRsearch design. - -These components can be seen as abstractions present in any testing procedure. -For example, there can be a single component acting both -as the Manager and the Controller, but as long as values of required attributes -of Search Goals and Goal Results are visible in the test report, -the Controller Input instance and output instance are implied. - -For example, any setup for conditionally (or unconditionally) -compliant throughput testing -can be understood as a MLRsearch architecture, -assuming there is enough data to reconstruct the Relevant Upper Bound. - -See [RFC2544 Goal] (#RFC2544-Goal) subsection for equivalent Search Goal. - -Any test procedure that can be understood as (one call to the Manager of) -MLRsearch architecture is said to be compliant with MLRsearch specification. - -
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Additional Considerations - -This section focuses on additional considerations, intuitions and motivations -pertaining to MLRsearch methodology. - - -
MLRsearch Versions - -The MLRsearch algorithm has been developed in a code-first approach, -a Python library has been created, debugged, used in production -and published in PyPI before the first descriptions -(even informal) were published. - -But the code (and hence the description) was evolving over time. -Multiple versions of the library were used over past several years, -and later code was usually not compatible with earlier descriptions. - -The code in (some version of) MLRsearch library fully determines -the search process (for a given set of configuration parameters), -leaving no space for deviations. - - - -This historic meaning of MLRsearch, as a family -of search algorithm implementations, -leaves plenty of space for future improvements, at the cost -of poor comparability of results of search algoritm implementations. - - -There are two competing needs. -There is the need for standardization in areas critical to comparability. -There is also the need to allow flexibility for implementations -to innovate and improve in other areas. -This document defines MLRsearch as a new specification -in a manner that aims to fairly balance both needs. - -
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Stopping Conditions - - prescribes that after performing one trial at a specific offered load, -the next offered load should be larger or smaller, based on frame loss. - -The usual implementation uses binary search. -Here a lossy trial becomes -a new upper bound, a lossless trial becomes a new lower bound. -The span of values between the tightest lower bound -and the tightest upper bound (including both values) forms an interval of possible results, -and after each trial the width of that interval halves. - -Usually the binary search implementation tracks only the two tightest bounds, -simply calling them bounds. -But the old values still remain valid bounds, -just not as tight as the new ones. - -After some number of trials, the tightest lower bound becomes the throughput. - does not specify when, if ever, should the search stop. - -MLRsearch introduces a concept of [Goal Width] (#Goal-Width). - -The search stops -when the distance between the tightest upper bound and the tightest lower bound -is smaller than a user-configured value, called Goal Width from now on. -In other words, the interval width at the end of the search -has to be no larger than the Goal Width. - -This Goal Width value therefore determines the precision of the result. -Due to the fact that MLRsearch specification requires a particular -structure of the result (see [Trial Result] (#Trial-Result) section), -the result itself does contain enough information to determine its -precision, thus it is not required to report the Goal Width value. - -This allows MLRsearch implementations to use stopping conditions -different from Goal Width. - -
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Load Classification - -MLRsearch keeps the basic logic of binary search (tracking tightest bounds, -measuring at the middle), perhaps with minor technical differences. - -MLRsearch algorithm chooses an intended load (as opposed to the offered load), -the interval between bounds does not need to be split -exactly into two equal halves, -and the final reported structure specifies both bounds. - -The biggest difference is that to classify a load -as an upper or lower bound, MLRsearch may need more than one trial -(depending on configuration options) to be performed at the same intended load. - -In consequence, even if a load already does have few trial results, -it still may be classified as undecided, neither a lower bound nor an upper bound. - -An explanation of the classification logic is given in the next section [Logic of Load Classification] (#Logic-of-Load-Classification), -as it heavily relies on other subsections of this section. - -For repeatability and comparability reasons, it is important that -given a set of trial results, all implementations of MLRsearch -classify the load equivalently. - -
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Loss Ratios - -Another difference between MLRsearch and binary search is in the goals of the search. - has a single goal, -based on classifying full-length trials as either lossless or lossy. - -MLRsearch, as the name suggests, can search for multiple goals, -differing in their loss ratios. -The precise definition of the Goal Loss Ratio will be given later. -The throughput goal then simply becomes a zero Goal Loss Ratio. -Different goals also may have different Goal Widths. - -A set of trial results for one specific intended load value -can classify the load as an upper bound for some goals, but a lower bound -for some other goals, and undecided for the rest of the goals. - -Therefore, the load classification depends not only on trial results, -but also on the goal. -The overall search procedure becomes more complicated, when -compared to binary search with a single goal, -but most of the complications do not affect the final result, -except for one phenomenon, loss inversion. - -
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Loss Inversion - -In throughput search using bisection, any load with a lossy trial -becomes a hard upper bound, meaning every subsequent trial has a smaller -intended load. - -But in MLRsearch, a load that is classified as an upper bound for one goal -may still be a lower bound for another goal, and due to the other goal -MLRsearch will probably perform trials at even higher loads. -What to do when all such higher load trials happen to have zero loss? -Does it mean the earlier upper bound was not real? -Does it mean the later lossless trials are not considered a lower bound? -Surely we do not want to have an upper bound at a load smaller than a lower bound. - -MLRsearch is conservative in these situations. -The upper bound is considered real, and the lossless trials at higher loads -are considered to be a coincidence, at least when computing the final result. - -This is formalized using new notions, the [Relevant Upper Bound] (#Relevant-Upper-Bound) and -the [Relevant Lower Bound] (#Relevant-Lower-Bound). -Load classification is still based just on the set of trial results -at a given intended load (trials at other loads are ignored), -making it possible to have a lower load classified as an upper bound, -and a higher load classified as a lower bound (for the same goal). -The Relevant Upper Bound (for a goal) is the smallest load classified -as an upper bound. -But the Relevant Lower Bound is not simply -the largest among lower bounds. -It is the largest load among loads -that are lower bounds while also being smaller than the Relevant Upper Bound. - -With these definitions, the Relevant Lower Bound is always smaller -than the Relevant Upper Bound (if both exist), and the two relevant bounds -are used analogously as the two tightest bounds in the binary search. -When they are less than the Goal Width apart, -the relevant bounds are used in the output. - -One consequence is that every trial result can have an impact on the search result. -That means if your SUT (or your traffic generator) needs a warmup, -be sure to warm it up before starting the search. - -
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Exceed Ratio - -The idea of performing multiple trials at the same load comes from -a model where some trial results (those with high loss) are affected -by infrequent effects, causing poor repeatability of throughput results. -See the discussion about noiseful and noiseless ends -of the SUT performance spectrum in section [DUT in SUT] (#DUT-in-SUT). -Stable results are closer to the noiseless end of the SUT performance spectrum, -so MLRsearch may need to allow some frequency of high-loss trials -to ignore the rare but big effects near the noiseful end. - -MLRsearch can do such trial result filtering, but it needs -a configuration option to tell it how frequent can the infrequent big loss be. -This option is called the exceed ratio. -It tells MLRsearch what ratio of trials -(more exactly what ratio of trial seconds) can have a [Trial Loss Ratio] (#Trial-Loss-Ratio) -larger than the Goal Loss Ratio and still be classified as a lower bound. -Zero exceed ratio means all trials have to have a Trial Loss Ratio -equal to or smaller than the Goal Loss Ratio. - -For explainability reasons, the RECOMMENDED value for exceed ratio is 0.5, -as it simplifies some later concepts by relating them to the concept of median. - -
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Duration Sum - -When more than one trial is intended to classify a load, -MLRsearch also needs something that controls the number of trials needed. -Therefore, each goal also has an attribute called duration sum. - -The meaning of a [Goal Duration Sum] (#Goal-Duration-Sum) is that -when a load has (full-length) trials -whose trial durations when summed up give a value at least as big -as the Goal Duration Sum value, -the load is guaranteed to be classified either as an upper bound -or a lower bound for that goal. - -Due to the fact that the duration sum has a big impact -on the overall search duration, and prescribes -wait intervals around trial traffic, -the MLRsearch algorithm is allowed to sum durations that are different -from the actual trial traffic durations. - -In the MLRsearch specification, the different duration values are called -[Trial Effective Duration] (#Trial-Effective-Duration). - -
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Short Trials - -MLRsearch requires each goal to specify its final trial duration. -Full-length trial is a shorter name for a trial whose intended trial duration -is equal to (or longer than) the goal final trial duration. - -Section 24 of already anticipates possible time savings -when short trials (shorter than full-length trials) are used. -Full-length trials are the opposite of short trials, -so they may also be called long trials. - -Any MLRsearch implementation may include its own configuration options -which control when and how MLRsearch chooses to use short trial durations. - -For explainability reasons, when exceed ratio of 0.5 is used, -it is recommended for the Goal Duration Sum to be an odd multiple -of the full trial durations, so Conditional Throughput becomes identical to -a median of a particular set of trial forwarding rates. - -The presence of short trial results complicates the load classification logic. - -Full details are given later in section [Logic of Load Classification] (#Logic-of-Load-Classification). -In a nutshell, results from short trials -may cause a load to be classified as an upper bound. -This may cause loss inversion, and thus lower the Relevant Lower Bound, -below what would classification say when considering full-length trials only. - - - -
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Throughput - - -Due to the fact that testing equipment takes the intended load as an input parameter -for a trial measurement, any load search algorithm needs to deal -with intended load values internally. - -But in the presence of goals with a non-zero loss ratio, the intended load -usually does not match the user's intuition of what a throughput is. -The forwarding rate (as defined in section 3.6.1) is better, -but it is not obvious how to generalize it -for loads with multiple trial results and a non-zero -[Goal Loss Ratio] (#Goal-Loss-Ratio). - -The best example is also the main motivation: hard limit performance. -Even if the medium allows higher performance, -the SUT interfaces may have their additional own limitations, -e.g. a specific fps limit on the NIC (a very common occurance). - -Ideally, those should be known and used when computing Max Load. -But if Max Load is higher that what interface can receive or transmit, -there will be a "hard limit" observed in trial results. -Imagine the hard limit is at 100 Mfps, Max Load is higher, -and the goal loss ratio is 0.5%. If DUT has no additional losses, -0.5% loss ratio will be achieved at 100.5025 Mfps (the relevant lower bound). -But it is not intuitive to report SUT performance as a value that is -larger than known hard limit. -We need a generalization of RFC2544 throughput, -different from just the relevant lower bound. - -MLRsearch defines one such generalization, called the Conditional Throughput. -It is the trial forwarding rate from one of the trials -performed at the load in question. -Determining which trial exactly is defined in -[MLRsearch Specification] (#MLRsearch-Specification), -and in [Appendix B: Conditional Throughput] (#Appendix-B:-Conditional-Throughput). - -In the hard limit example, 100.5 Mfps load will still have -only 100.0 Mfps forwarding rate, nicely confirming the known limitation. - -Conditional Throughput is partially related to load classification. -If a load is classified as a lower bound for a goal, -the Conditional Throughput can be calculated from trial results, -and guaranteed to show an loss ratio -no larger than the Goal Loss Ratio. - - - - -Note that when comparing the best (all zero loss) and worst case (all loss -just below Goal Loss Ratio), the same Relevant Lower Bound value -may result in the Conditional Throughput differing up to the Goal Loss Ratio. - -Therefore it is rarely needed to set the Goal Width (if expressed -as the relative difference of loads) below the Goal Loss Ratio. -In other words, setting the Goal Width below the Goal Loss Ratio -may cause the Conditional Throughput for a larger loss ratio to become smaller -than a Conditional Throughput for a goal with a smaller Goal Loss Ratio, -which is counter-intuitive, considering they come from the same search. -Therefore it is RECOMMENDED to set the Goal Width to a value no smaller -than the Goal Loss Ratio. - -Overall, this Conditional Throughput does behave well for comparability purposes. - -
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Search Time - -MLRsearch was primarily developed to reduce the time -required to determine a throughput, either the compliant one, -or some generalization thereof. -The art of achieving short search times -is mainly in the smart selection of intended loads (and intended durations) -for the next trial to perform. - -While there is an indirect impact of the load selection on the reported values, -in practice such impact tends to be small, -even for SUTs with quite a broad performance spectrum. - -A typical example of two approaches to load selection leading to different -Relevant Lower Bounds is when the interval is split in a very uneven way. -Any implementation choosing loads very close to the current Relevant Lower Bound -is quite likely to eventually stumble upon a trial result -with poor performance (due to SUT noise). -For an implementation choosing loads very close -to the current Relevant Upper Bound, this is unlikely, -as it examines more loads that can see a performance -close to the noiseless end of the SUT performance spectrum. - -However, as even splits optimize search duration at give precision, -MLRsearch implementations that prioritize minimizing search time -are unlikely to suffer from any such bias. - -Therefore, this document remains quite vague on load selection -and other optimization details, and configuration attributes related to them. -Assuming users prefer libraries that achieve short overall search time, -the definition of the Relevant Lower Bound -should be strict enough to ensure result repeatability -and comparability between different implementations, -while not restricting future implementations much. - - -
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Compliance - -Some Search Goal instances lead to results compliant with RFC2544. -See [RFC2544 Goal] (#RFC2544-Goal) for more details -regarding both conditional and unconditional compliance. - -The presence of other Search Goals does not affect the compliance -of this Goal Result. -The Relevant Lower Bound and the Conditional Throughput are in this case -equal to each other, and the value is the throughput. - -
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Logic of Load Classification - -
Introductory Remarks - -This chapter continues with explanations, -but this time more precise definitions are needed -for readers to follow the explanations. - -Descriptions in this section are wordy and implementers should read -[MLRsearch Specification] (#MLRsearch-Specification) section -and Appendices for more concise definitions. - -The two areas of focus here are load classification -and the Conditional Throughput. - -To start with [Performance Spectrum] (#Performance-Spectrum) -subsection contains definitions needed to gain insight -into what Conditional Throughput means. -Remaining subsections discuss load classification. - -For load classification, it is useful to define good trials and bad trials: - - - Bad trial: Trial is called bad (according to a goal) -if its [Trial Loss Ratio] (#Trial-Loss-Ratio) -is larger than the [Goal Loss Ratio] (#Goal-Loss-Ratio). - Good trial: Trial that is not bad is called good. - - -
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Performance Spectrum -### Description - -There are several equivalent ways to explain the Conditional Throughput -computation. One of the ways relies on performance -spectrum. - -Take an intended load value, a trial duration value, and a finite set -of trial results, with all trials measured at that load value and duration value. - -The performance spectrum is the function that maps -any non-negative real number into a sum of trial durations among all trials -in the set, that has that number, as their trial forwarding rate, -e.g. map to zero if no trial has that particular forwarding rate. - -A related function, defined if there is at least one trial in the set, -is the performance spectrum divided by the sum of the durations -of all trials in the set. - -That function is called the performance probability function, as it satisfies -all the requirements for probability mass function -of a discrete probability distribution, -the one-dimensional random variable being the trial forwarding rate. - -These functions are related to the SUT performance spectrum, -as sampled by the trials in the set. - - -Take a set of all full-length trials performed at the Relevant Lower Bound, -sorted by decreasing trial forwarding rate. -The sum of the durations of those trials -may be less than the Goal Duration Sum, or not. -If it is less, add an imaginary trial result with zero trial forwarding rate, -such that the new sum of durations is equal to the Goal Duration Sum. -This is the set of trials to use. - -If the quantile touches two trials, - - -the larger trial forwarding rate (from the trial result sorted earlier) is used. - - -The resulting quantity is the Conditional Throughput of the goal in question. - - -A set of examples follows. - -
First Example - - - [Goal Exceed Ratio] (#Goal-Exceed-Ratio) = 0 and [Goal Duration Sum] (#Goal-Duration-Sum) has been reached. - Conditional Throughput is the smallest trial forwarding rate among the trials. - - -
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Second Example - - - Goal Exceed Ratio = 0 and Goal Duration Sum has not been reached yet. - Due to the missing duration sum, the worst case may still happen, so the Conditional Throughput is zero. - This is not reported to the user, as this load cannot become the Relevant Lower Bound yet. - - -
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Third Example - - - Goal Exceed Ratio = 50% and Goal Duration Sum is two seconds. - One trial is present with the duration of one second and zero loss. - The imaginary trial is added with the duration of one second and zero trial forwarding rate. - The median would touch both trials, so the Conditional Throughput is the trial forwarding rate of the one non-imaginary trial. - As that had zero loss, the value is equal to the offered load. - - - -
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Summary - -While the Conditional Throughput is a generalization of the trial forwarding rate, -its definition is not an obvious one. - -Other than the trial forwarding rate, the other source of intuition -is the quantile in general, and the median the recommended case. - - -
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Trials with Single Duration - - -When goal attributes are chosen in such a way that every trial has the same -intended duration, the load classification is simpler. - -The following description follows the motivation -of Goal Loss Ratio, Goal Exceed Ratio, and Goal Duration Sum. - -If the sum of the durations of all trials (at the given load) -is less than the Goal Duration Sum, imagine two scenarios: - - - best case scenario: all subsequent trials having zero loss, and - worst case scenario: all subsequent trials having 100% loss. - - -Here we assume there are as many subsequent trials as needed -to make the sum of all trials equal to the Goal Duration Sum. - -The exceed ratio is defined using sums of durations -(and number of trials does not matter), so it does not matter whether -the "subsequent trials" can consist of an integer number of full-length trials. - -In any of the two scenarios, best case and worst case, we can compute the load exceed ratio, -as the duration sum of good trials divided by the duration sum of all trials, -in both cases including the assumed trials. - -Even if, in the best case scenario, the load exceed ratio is larger -than the Goal Exceed Ratio, the load is an upper bound. - -MKP2 Even if, in the worst case scenario, the load exceed ratio is not larger -than the Goal Exceed Ratio, the load is a lower bound. - - -More specifically: - - - Take all trials measured at a given load. - The sum of the durations of all bad full-length trials is called the bad sum. - The sum of the durations of all good full-length trials is called the good sum. - The result of adding the bad sum plus the good sum is called the measured sum. - The larger of the measured sum and the Goal Duration Sum is called the whole sum. - The whole sum minus the measured sum is called the missing sum. - The optimistic exceed ratio is the bad sum divided by the whole sum. - The pessimistic exceed ratio is the bad sum plus the missing sum, that divided by the whole sum. - If the optimistic exceed ratio is larger than the Goal Exceed Ratio, the load is classified as an upper bound. - If the pessimistic exceed ratio is not larger than the Goal Exceed Ratio, the load is classified as a lower bound. - Else, the load is classified as undecided. - - -The definition of pessimistic exceed ratio is compatible with the logic in -the Conditional Throughput computation, so in this single trial duration case, -a load is a lower bound if and only if the Conditional Throughput -loss ratio is not larger than the Goal Loss Ratio. - - -If it is larger, the load is either an upper bound or undecided. - -
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Trials with Short Duration - -
Scenarios - -Trials with intended duration smaller than the goal final trial duration -are called short trials. -The motivation for load classification logic in the presence of short trials -is based around a counter-factual case: What would the trial result be -if a short trial has been measured as a full-length trial instead? - -There are three main scenarios where human intuition guides -the intended behavior of load classification. - -
False Good Scenario - -The user had their reason for not configuring a shorter goal -final trial duration. -Perhaps SUT has buffers that may get full at longer -trial durations. -Perhaps SUT shows periodic decreases in performance -the user does not want to be treated as noise. - -In any case, many good short trials may become bad full-length trials -in the counter-factual case. - -In extreme cases, there are plenty of good short trials and no bad short trials. - -In this scenario, we want the load classification NOT to classify the load -as a lower bound, despite the abundance of good short trials. - - -Effectively, we want the good short trials to be ignored, so they -do not contribute to comparisons with the Goal Duration Sum. - -
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True Bad Scenario - -When there is a frame loss in a short trial, -the counter-factual full-length trial is expected to lose at least as many -frames. - -In practice, bad short trials are rarely turning into -good full-length trials. - -In extreme cases, there are no good short trials. - -In this scenario, we want the load classification -to classify the load as an upper bound just based on the abundance -of short bad trials. - -Effectively, we want the bad short trials -to contribute to comparisons with the Goal Duration Sum, -so the load can be classified sooner. - -
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Balanced Scenario - -Some SUTs are quite indifferent to trial duration. -Performance probability function constructed from short trial results -is likely to be similar to the performance probability function constructed -from full-length trial results (perhaps with larger dispersion, -but without a big impact on the median quantiles overall). - - -For a moderate Goal Exceed Ratio value, this may mean there are both -good short trials and bad short trials. - -This scenario is there just to invalidate a simple heuristic -of always ignoring good short trials and never ignoring bad short trials, -as that simple heuristic would be too biased. - -Yes, the short bad trials -are likely to turn into full-length bad trials in the counter-factual case, -but there is no information on what would the good short trials turn into. - -The only way to decide safely is to do more trials at full length, -the same as in False Good Scenario. - -
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Classification Logic - -MLRsearch picks a particular logic for load classification -in the presence of short trials, but it is still RECOMMENDED -to use configurations that imply no short trials, -so the possible inefficiencies in that logic -do not affect the result, and the result has better explainability. - -With that said, the logic differs from the single trial duration case -only in different definition of the bad sum. -The good sum is still the sum across all good full-length trials. - -Few more notions are needed for defining the new bad sum: - - - The sum of durations of all bad full-length trials is called the bad long sum. - The sum of durations of all bad short trials is called the bad short sum. - The sum of durations of all good short trials is called the good short sum. - One minus the Goal Exceed Ratio is called the subceed ratio. - The Goal Exceed Ratio divided by the subceed ratio is called the exceed coefficient. - The good short sum multiplied by the exceed coefficient is called the balancing sum. - The bad short sum minus the balancing sum is called the excess sum. - If the excess sum is negative, the bad sum is equal to the bad long sum. - Otherwise, the bad sum is equal to the bad long sum plus the excess sum. - - -Here is how the new definition of the bad sum fares in the three scenarios, -where the load is close to what would the relevant bounds be -if only full-length trials were used for the search. - -
False Good Scenario - -If the duration is too short, we expect to see a higher frequency -of good short trials. -This could lead to a negative excess sum, -which has no impact, hence the load classification is given just by -full-length trials. -Thus, MLRsearch using too short trials has no detrimental effect -on result comparability in this scenario. -But also using short trials does not help with overall search duration, -probably making it worse. - -
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True Bad Scenario - -Settings with a small exceed ratio -have a small exceed coefficient, so the impact of the good short sum is small, -and the bad short sum is almost wholly converted into excess sum, -thus bad short trials have almost as big an impact as full-length bad trials. -The same conclusion applies to moderate exceed ratio values -when the good short sum is small. -Thus, short trials can cause a load to get classified as an upper bound earlier, -bringing time savings (while not affecting comparability). - -
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Balanced Scenario - -Here excess sum is small in absolute value, as the balancing sum -is expected to be similar to the bad short sum. -Once again, full-length trials are needed for final load classification; -but usage of short trials probably means MLRsearch needed -a shorter overall search time before selecting this load for measurement, -thus bringing time savings (while not affecting comparability). - -Note that in presence of short trial results, -the comparibility between the load classification -and the Conditional Throughput is only partial. -The Conditional Throughput still comes from a good long trial, -but a load higher than the Relevant Lower Bound may also compute to a good value. - -
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Trials with Longer Duration - -If there are trial results with an intended duration larger -than the goal trial duration, the precise definitions -in Appendix A and Appendix B treat them in exactly the same way -as trials with duration equal to the goal trial duration. - -But in configurations with moderate (including 0.5) or small -Goal Exceed Ratio and small Goal Loss Ratio (especially zero), -bad trials with longer than goal durations may bias the search -towards the lower load values, as the noiseful end of the spectrum -gets a larger probability of causing the loss within the longer trials. - - - - -
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IANA Considerations - -No requests of IANA. - -
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Security Considerations - -Benchmarking activities as described in this memo are limited to -technology characterization of a DUT/SUT using controlled stimuli in a -laboratory environment, with dedicated address space and the constraints -specified in the sections above. - -The benchmarking network topology will be an independent test setup and -MUST NOT be connected to devices that may forward the test traffic into -a production network or misroute traffic to the test management network. - -Further, benchmarking is performed on a "black-box" basis, relying -solely on measurements observable external to the DUT/SUT. - -Special capabilities SHOULD NOT exist in the DUT/SUT specifically for -benchmarking purposes. Any implications for network security arising -from the DUT/SUT SHOULD be identical in the lab and in production -networks. - -
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Acknowledgements - -Some phrases and statements in this document were created -with help of Mistral AI (mistral.ai). - -Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough -review and numerous useful comments and suggestions in the earlier versions of this document. - -Special wholehearted gratitude and thanks to the late Al Morton for his -thorough reviews filled with very specific feedback and constructive -guidelines. Thank you Al for the close collaboration over the years, -for your continuous unwavering encouragement full of empathy and -positive attitude. Al, you are dearly missed. - -
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Appendix A: Load Classification - -This section specifies how to perform the load classification. - -Any intended load value can be classified, according to a given [Search Goal] (#Search-Goal). - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the Measurer. - -The block at the end of this appendix holds pseudocode -which computes two values, stored in variables named -optimistic and pessimistic. - - -The pseudocode happens to be a valid Python code. - -If values of both variables are computed to be true, the load in question -is classified as a lower bound according to the given Search Goal. -If values of both variables are false, the load is classified as an upper bound. -Otherwise, the load is classified as undecided. - -The pseudocode expects the following variables to hold values as follows: - - - goal_duration_sum: The duration sum value of the given Search Goal. - goal_exceed_ratio: The exceed ratio value of the given Search Goal. - good_long_sum: Sum of durations across trials with trial duration -at least equal to the goal final trial duration and with a Trial Loss Ratio -not higher than the Goal Loss Ratio. - bad_long_sum: Sum of durations across trials with trial duration -at least equal to the goal final trial duration and with a Trial Loss Ratio -higher than the Goal Loss Ratio. - good_short_sum: Sum of durations across trials with trial duration -shorter than the goal final trial duration and with a Trial Loss Ratio -not higher than the Goal Loss Ratio. - bad_short_sum: Sum of durations across trials with trial duration -shorter than the goal final trial duration and with a Trial Loss Ratio -higher than the Goal Loss Ratio. - - -The code works correctly also when there are no trial results at a given load. - -
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Appendix B: Conditional Throughput - -This section specifies how to compute Conditional Throughput, as referred to in section [Conditional Throughput] (#Conditional-Throughput). - -Any intended load value can be used as the basis for the following computation, -but only the Relevant Lower Bound (at the end of the search) -leads to the value called the Conditional Throughput for a given Search Goal. - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the Measurer. - -The block at the end of this appendix holds pseudocode -which computes a value stored as variable conditional_throughput. - - -The pseudocode happens to be a valid Python code. - -The pseudocode expects the following variables to hold values as follows: - - - goal_duration_sum: The duration sum value of the given Search Goal. - goal_exceed_ratio: The exceed ratio value of the given Search Goal. - good_long_sum: Sum of durations across trials with trial duration -at least equal to the goal final trial duration and with a Trial Loss Ratio -not higher than the Goal Loss Ratio. - bad_long_sum: Sum of durations across trials with trial duration -at least equal to the goal final trial duration and with a Trial Loss Ratio -higher than the Goal Loss Ratio. - long_trials: An iterable of all trial results from trials with trial duration -at least equal to the goal final trial duration, -sorted by increasing the Trial Loss Ratio. -A trial result is a composite with the following two attributes available: - trial.loss_ratio: The Trial Loss Ratio as measured for this trial. - trial.duration: The trial duration of this trial. - - - -The code works correctly only when there if there is at least one -trial result measured at a given load. - -
0.0: - quantile_loss_ratio = trial.loss_ratio - remaining -= trial.duration - else: - break -else: - if remaining > 0.0: - quantile_loss_ratio = 1.0 -conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) -]]>
- -
- - - - - - - - - - - -&RFC1242; -&RFC2285; -&RFC2544; -&RFC8219; -&RFC9004; - - - - - - - - - TST 009 - - - - - - - - - FD.io CSIT Test Methodology - MLRsearch - - - - - - - - - MLRsearch 1.2.1, Python Package Index - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md new file mode 100644 index 0000000000..387ff4dba8 --- /dev/null +++ b/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md @@ -0,0 +1,3123 @@ +--- + +title: Multiple Loss Ratio Search +abbrev: MLRsearch +docname: draft-ietf-bmwg-mlrsearch-08 +date: 2024-08-28 + +ipr: trust200902 +area: ops +wg: Benchmarking Working Group +kw: Internet-Draft +cat: info + +coding: us-ascii +pi: # can use array (if all yes) or hash here + toc: yes + sortrefs: # defaults to yes + symrefs: yes + +author: + - + ins: M. Konstantynowicz + name: Maciek Konstantynowicz + org: Cisco Systems + email: mkonstan@cisco.com + - + ins: V. Polak + name: Vratko Polak + org: Cisco Systems + email: vrpolak@cisco.com + +normative: + RFC1242: + RFC2285: + RFC2544: + RFC8219: + RFC9004: + +informative: + TST009: + target: https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.04.01_60/gs_NFV-TST009v030401p.pdf + title: "TST 009" + FDio-CSIT-MLRsearch: + target: https://csit.fd.io/cdocs/methodology/measurements/data_plane_throughput/mlr_search/ + title: "FD.io CSIT Test Methodology - MLRsearch" + date: 2023-10 + PyPI-MLRsearch: + target: https://pypi.org/project/MLRsearch/1.2.1/ + title: "MLRsearch 1.2.1, Python Package Index" + date: 2023-10 + +--- abstract + +This document proposes extensions to [RFC2544] throughput search by +defining a new methodology called Multiple Loss Ratio search +(MLRsearch). MLRsearch aims to minimize search duration, +support multiple loss ratio searches, +and enhance result repeatability and comparability. + +The primary reason for extending [RFC2544] is to address the challenges +and requirements presented by the evaluation and testing +of software-based networking systems' data planes. + +To give users more freedom, MLRsearch provides additional configuration options +such as allowing multiple short trials per load instead of one large trial, +tolerating a certain percentage of trial results with higher loss, +and supporting the search for multiple goals with varying loss ratios. + +--- middle + +{::comment} + + As we use Kramdown to convert from Markdown, + we use this way of marking comments not to be visible in the rendered draft. + https://stackoverflow.com/a/42323390 + If another engine is used, convert to this way: + https://stackoverflow.com/a/20885980 + + [toc] + +{:/comment} + + +# Purpose and Scope + +The purpose of this document is to describe Multiple Loss Ratio search +(MLRsearch), a data plane throughput search methodology optimized for software +networking DUTs. + +Applying vanilla [RFC2544] throughput bisection to software DUTs +results in several problems: + +- Binary search takes too long as most trials are done far from the + eventually found throughput. +- The required final trial duration and pauses between trials + prolong the overall search duration. +- Software DUTs show noisy trial results, + leading to a big spread of possible discovered throughput values. +- Throughput requires a loss of exactly zero frames, but the industry + frequently allows for small but non-zero losses. +- The definition of throughput is not clear when trial results are inconsistent. + +To address the problems mentioned above, +the MLRsearch test methodology specification employs the following enhancements: + +- Allow multiple short trials instead of one big trial per load. + - Optionally, tolerate a percentage of trial results with higher loss. +- Allow searching for multiple Search Goals, with differing loss ratios. + - Any trial result can affect each Search Goal in principle. +- Insert multiple coarse targets for each Search Goal, earlier ones need + to spend less time on trials. + - Earlier targets also aim for lesser precision. + - Use Forwarding Rate (FR) at maximum offered load + [RFC2285] (section 3.6.2) to initialize the initial targets. +- Take care when dealing with inconsistent trial results. + - Reported throughput is smaller than the smallest load with high loss. + - Smaller load candidates are measured first. +- Apply several load selection heuristics to save even more time + by trying hard to avoid unnecessarily narrow bounds. + +Some of these enhancements are formalized as MLRsearch specification, +the remaining enhancements are treated as implementation details, +thus achieving high comparability without limiting future improvements. + +MLRsearch configuration options are flexible enough to +support both conservative settings and aggressive settings. +The conservative settings lead to results +unconditionally compliant with [RFC2544], +but longer search duration and worse repeatability. +Conversely, aggressive settings lead to shorter search duration +and better repeatability, but the results are not compliant with [RFC2544]. + +No part of [RFC2544] is intended to be obsoleted by this document. + +# Identified Problems + +This chapter describes the problems affecting usability +of various performance testing methodologies, +mainly a binary search for [RFC2544] unconditionally compliant throughput. + +## Long Search Duration + +{::comment} + [Low priority] + + MKP2 [VP] TODO: Look for mentions of search duration in existing RFCs. + + MKP2 [VP] TODO: If not found, define right after defining "the search". + +{:/comment} + +The emergence of software DUTs, with frequent software updates and a +number of different frame processing modes and configurations, +has increased both the number of performance tests +required to verify the DUT update and the frequency of running those tests. +This makes the overall test execution time even more important than before. + +The current [RFC2544] throughput definition restricts the potential +for time-efficiency improvements. +A more generalized throughput concept could enable further enhancements +while maintaining the precision of simpler methods. + +The bisection method, when unconditionally compliant with [RFC2544], +is excessively slow. +This is because a significant amount of time is spent on trials +with loads that, in retrospect, are far from the final determined throughput. + +[RFC2544] does not specify any stopping condition for throughput search, +so users already have an access to a limited trade-off +between search duration and achieved precision. +However, each full 60-second trials doubles the precision, +so not many trials can be removed without a substantial loss of precision. + +## DUT in SUT + +[RFC2285] defines: +- DUT as + - The network forwarding device to which stimulus is offered and + response measured [RFC2285] (section 3.1.1). +- SUT as + - The collective set of network devices to which stimulus is offered + as a single entity and response measured [RFC2285] (section 3.1.2). + +[RFC2544] specifies a test setup with an external tester stimulating the +networking system, treating it either as a single DUT, or as a system +of devices, an SUT. + +In the case of software networking, the SUT consists of not only the DUT +as a software program processing frames, but also of +server hardware and operating system functions, +with that server hardware resources shared across all programs including +the operating system. + +Given that the SUT is a shared multi-tenant environment +encompassing the DUT and other components, the DUT might inadvertently +experience interference from the operating system +or other software operating on the same server. + +Some of this interference can be mitigated. +For instance, +pinning DUT program threads to specific CPU cores +and isolating those cores can prevent context switching. + +Despite taking all feasible precautions, some adverse effects may still impact +the DUT's network performance. +In this document, these effects are collectively +referred to as SUT noise, even if the effects are not as unpredictable +as what other engineering disciplines call noise. + +DUT can also exhibit fluctuating performance itself, for reasons +not related to the rest of SUT. For example due to pauses in execution +as needed for internal stateful processing. +In many cases this +may be an expected per-design behavior, as it would be observable even +in a hypothetical scenario where all sources of SUT noise are eliminated. +Such behavior affects trial results in a way similar to SUT noise. +As the two phenomenons are hard to distinguish, +in this document the term 'noise' is used to encompass +both the internal performance fluctuations of the DUT +and the genuine noise of the SUT. + +A simple model of SUT performance consists of an idealized noiseless performance, +and additional noise effects. +For a specific SUT, the noiseless performance is assumed to be constant, +with all observed performance variations being attributed to noise. +The impact of the noise can vary in time, sometimes wildly, +even within a single trial. +The noise can sometimes be negligible, but frequently +it lowers the observed SUT performance as observed in trial results. + +In this model, SUT does not have a single performance value, it has a spectrum. +One end of the spectrum is the idealized noiseless performance value, +the other end can be called a noiseful performance. +In practice, trial result +close to the noiseful end of the spectrum happens only rarely. +The worse the performance value is, the more rarely it is seen in a trial. +Therefore, the extreme noiseful end of the SUT spectrum is not observable +among trial results. +Also, the extreme noiseless end of the SUT spectrum +is unlikely to be observable, this time because some small noise effects +are likely to occur multiple times during a trial. + +Unless specified otherwise, this document's focus is +on the potentially observable ends of the SUT performance spectrum, +as opposed to the extreme ones. + +When focusing on the DUT, the benchmarking effort should ideally aim +to eliminate only the SUT noise from SUT measurements. +However, +this is currently not feasible in practice, as there are no realistic enough +models available to distinguish SUT noise from DUT fluctuations, +based on authors' experience and available literature. + +Assuming a well-constructed SUT, the DUT is likely its +primary performance bottleneck. +In this case, we can define the DUT's +ideal noiseless performance as the noiseless end of the SUT performance spectrum, +especially for throughput. +However, other performance metrics, such as latency, +may require additional considerations. + +Note that by this definition, DUT noiseless performance +also minimizes the impact of DUT fluctuations, as much as realistically possible +for a given trial duration. + +MLRsearch methodology aims to solve the DUT in SUT problem +by estimating the noiseless end of the SUT performance spectrum +using a limited number of trial results. + +Any improvements to the throughput search algorithm, aimed at better +dealing with software networking SUT and DUT setup, should employ +strategies recognizing the presence of SUT noise, allowing the discovery of +(proxies for) DUT noiseless performance +at different levels of sensitivity to SUT noise. + +## Repeatability and Comparability + +[RFC2544] does not suggest to repeat throughput search. +And from just one +discovered throughput value, it cannot be determined how repeatable that value is. +Poor repeatability then leads to poor comparability, +as different benchmarking teams may obtain varying throughput values +for the same SUT, exceeding the expected differences from search precision. + +[RFC2544] throughput requirements (60 seconds trial and +no tolerance of a single frame loss) affect the throughput results +in the following way. +The SUT behavior close to the noiseful end of its performance spectrum +consists of rare occasions of significantly low performance, +but the long trial duration makes those occasions not so rare on the trial level. +Therefore, the binary search results tend to wander away from the noiseless end +of SUT performance spectrum, more frequently and more widely than short +trials would, thus causing poor throughput repeatability. + +The repeatability problem can be addressed by defining a search procedure +that identifies a consistent level of performance, +even if it does not meet the strict definition of throughput in [RFC2544]. + +According to the SUT performance spectrum model, better repeatability +will be at the noiseless end of the spectrum. +Therefore, solutions to the DUT in SUT problem +will help also with the repeatability problem. + +Conversely, any alteration to [RFC2544] throughput search +that improves repeatability should be considered +as less dependent on the SUT noise. + +An alternative option is to simply run a search multiple times, and report some +statistics (e.g. average and standard deviation). +This can be used +for a subset of tests deemed more important, +but it makes the search duration problem even more pronounced. + +## Throughput with Non-Zero Loss + +[RFC1242] (section 3.17 Throughput) defines throughput as: + The maximum rate at which none of the offered frames + are dropped by the device. + +Then, it says: + Since even the loss of one frame in a + data stream can cause significant delays while + waiting for the higher level protocols to time out, + it is useful to know the actual maximum data + rate that the device can support. + +However, many benchmarking teams accept a small, +non-zero loss ratio as the goal for their load search. + +Motivations are many: + +- Modern protocols tolerate frame loss better, + compared to the time when [RFC1242] and [RFC2544] were specified. + +- Trials nowadays send way more frames within the same duration, + increasing the chance of a small SUT performance fluctuation + being enough to cause frame loss. + +- Small bursts of frame loss caused by noise have otherwise smaller impact + on the average frame loss ratio observed in the trial, + as during other parts of the same trial the SUT may work more closely + to its noiseless performance, thus perhaps lowering the Trial Loss Ratio + below the Goal Loss Ratio value. + +- If an approximation of the SUT noise impact on the Trial Loss Ratio is known, + it can be set as the Goal Loss Ratio. + +Regardless of the validity of all similar motivations, +support for non-zero loss goals makes any search algorithm more user-friendly. +[RFC2544] throughput is not user-friendly in this regard. + +Furthermore, allowing users to specify multiple loss ratio values, +and enabling a single search to find all relevant bounds, +significantly enhances the usefulness of the search algorithm. + +Searching for multiple Search Goals also helps to describe the SUT performance +spectrum better than the result of a single Search Goal. +For example, the repeated wide gap between zero and non-zero loss loads +indicates the noise has a large impact on the observed performance, +which is not evident from a single goal load search procedure result. + +It is easy to modify the vanilla bisection to find a lower bound +for the intended load that satisfies a non-zero Goal Loss Ratio. +But it is not that obvious how to search for multiple goals at once, +hence the support for multiple Search Goals remains a problem. + +## Inconsistent Trial Results + +While performing throughput search by executing a sequence of +measurement trials, there is a risk of encountering inconsistencies +between trial results. + +The plain bisection never encounters inconsistent trials. +But [RFC2544] hints about the possibility of inconsistent trial results, +in two places in its text. +The first place is section 24, where full trial durations are required, +presumably because they can be inconsistent with the results +from short trial durations. +The second place is section 26.3, where two successive zero-loss trials +are recommended, presumably because after one zero-loss trial +there can be a subsequent inconsistent non-zero-loss trial. + +Examples include: + +- A trial at the same load (same or different trial duration) results + in a different Trial Loss Ratio. +- A trial at a higher load (same or different trial duration) results + in a smaller Trial Loss Ratio. + +Any robust throughput search algorithm needs to decide how to continue +the search in the presence of such inconsistencies. +Definitions of throughput in [RFC1242] and [RFC2544] are not specific enough +to imply a unique way of handling such inconsistencies. + +Ideally, there will be a definition of a new quantity which both generalizes +throughput for non-zero-loss (and other possible repeatability enhancements), +while being precise enough to force a specific way to resolve trial result +inconsistencies. +But until such a definition is agreed upon, the correct way to handle +inconsistent trial results remains an open problem. + +# MLRsearch Specification + +This section describes MLRsearch specification including all technical +definitions needed for evaluating whether a particular test procedure +complies with MLRsearch specification. + +{::comment} + [Good idea for 08, maybe ask BMWG first?] + + TODO VP: Separate Requirements and Recommendations/Suggestions + paragraphs? (currently requirements are in discussion subsections - + discussion should only clarify things without adding new + requirements) + +{:/comment} + +## Overview + +MLRsearch specification describes a set of abstract system components, +acting as functions with specified inputs and outputs. + +A test procedure is said to comply with MLRsearch specification +if it can be conceptually divided into analogous components, +each satisfying requirements for the corresponding MLRsearch component. + +The Measurer component is tasked to perform trials, +the Controller component is tasked to select trial loads and durations, +the Manager component is tasked to pre-configure everything +and to produce the test report. +The test report explicitly states Search Goals (as the Controller Inputs) +and corresponding Goal Results (Controller Outputs). + +{::comment} + [Low priority] + + MKP2 TODO: Find a good reference for the test report, seems only implicit in RFC2544. + +{:/comment} + +The Manager calls the Controller once, +the Controller keeps calling the Measurer +until all stopping conditions are met. + +The part where Controller calls the Measurer is called the search. +Any activity done by the Manager before it calls the Controller +(or after Controller returns) is not considered to be part of the search. + +MLRsearch specification prescribes regular search results and recommends +their stopping conditions. Irregular search results are also allowed, +they may have different requirements and stopping conditions. + +Search results are based on load classification. +When measured enough, any chosen load either achieves of fails each search goal, +thus becoming a lower or an upper bound for that goal. +When the relevant bounds are at loads that are close enough +(according to goal precision), the regular result is found. +Search stops when all regular results are found +(or if some goals are proven to have only irregular results). + +## Measurement Quantities + +MLRsearch specification uses a number of measurement quantities. + +In general, MLRsearch specification does not require particular units to be used, +but it is REQUIRED for the test report to state all the units. +For example, ratio quantities can be dimensionless numbers between zero and one, +but may be expressed as percentages instead. + +For convenience, a group of quantities can be treated as a composite quantity, +One constituent of a composite quantity is called an attribute, +and a group of attribute values is called an instance of that composite quantity. + +Some attributes are not independent from others, +and they can be calculated from other attributes. +Such quantites are called derived quantities. + +## Existing Terms + +RFC 1242 "Benchmarking Terminology for Network Interconnect Devices" +contains basic definitions, and +RFC 2544 "Benchmarking Methodology for Network Interconnect Devices" +contains discussions of a number of terms and additional methodology requirements. +RFC 2285 adds more terms and discussions, describing some known situations +in more precise way. + +All three documents should be consulted +before attempting to make use of this document. + +Definitions of some central terms are copied and discussed in subsections. + +{::comment} + [Good idea for 08, but needs more work. Ask BMWG?] + + Alternatively, quick list of all (existing and new here) terms, + with links (external or internal respectively) to definitions. + + MKP3 [VP] TODO: Even if the following list will not be in final draft, + it is useful to keep it around (maybe commented-out) while editing. + + MKP3 VP note: rough list of all RFC references: + - [RFC1242] (section 3.17 Throughput) ... definition + - [RFC2544] (section 26.1 Throughput) ... methodology + - [RFC2544] (section 24. Trial duration): + - full trial durations (implies short trials) + - Also 60s for unconditional compliance is here. + - Also "the search" (without quotes) appears there. + - Also "binary search" (with quotes) appears there. + - [RFC2544] (section 26.3 Frame loss rate): + - two successive zero-loss trials are recommended (hints about loss inversion) + - un/conditionally compliant with [RFC2544] + - [RFC2544] (section 26. Benchmarking tests:) + - all its "dot sections" have "Reporting format:" paragraphs + - (implies test report) + - [RFC2544] (section 26.1 Throughput) wants graph, frame size on X axis. + - [RFC2544] (section 23. Trial description) trial + - general description of trial + - wait times specifically, maybe also learning frames? + - Constant Load of [RFC1242] (section 3.4 Constant Load) + - Data Rate of [RFC2544] (section 14. Bidirectional traffic) + - seems equal to input frame rate [RFC2544] (23. Trial description). + - [RFC2544] (section 21. Bursty traffic) suggests non-constant loads? + - Intended Load of [RFC2285] (section 3.5.1 Intended load (Iload)) + - [RFC2285] (Section 3.5.2 Offered load (Oload)) + - Frame Loss Rate of [RFC1242] (section 3.6 Frame Loss Rate) + - Forwarding Rate as defined in [RFC2285] (section 3.6.1 Forwarding rate (FR)) + - [RFC2544] (section 20. Maximum frame rate) + - [RFC2285] (3.5.3 Maximum offered load (MOL)) + - reordered frames [RFC2544] (section 10. Verifying received frames) + - For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, + as recommended from [RFC2544] (section 8. Frame formats) + and [RFC2544] (section 12. Protocol addresses). + - [RFC8219] (section 5.3. Traffic Setup) introduces traffic setups consisting of a mix of IPv4 and IPv6 traffic + - [RFC2544] (section 9. Frame sizes) + - [RFC1242] (section 3.5 Data link frame size) + - [RFC2285] (section 3.6.2) FRMOL + - [RFC2285] (section 3.1.1) DUT + - [RFC2285] (section 3.1.2) SUT + - [RFC2544] (section 6. Test set up) test setup with (an external) tester + - [RFC9004] B2B + - [RFC8219] (section 5.3. Traffic Setup) for an example of ip4+ip6 mixed traffic + + + MKP3 [VP] TODO: Do not mention those that do not need discussion here. + +{:/comment} + + +{::comment} + [Low priority] + + MKP3 [VP] TODO: Do we even need RFC9004? + +{:/comment} + +{::comment} + [I do not understand what I meant. Typos? Probably not important overall.] + + MKP2 [VP] TODO: Even terms that are discussed in this memo, + they perhaps do not need a separate list (just free paragraphs), + in a chapter after MLRsearch specification. + +{:/comment} + +{::comment} + [Important, just not enough time in 07.] + + MKP3 [VP] TODO: Verify that MLRsearch specification does not discuss + meaning of existing terms without quoting their original definition. + +{:/comment} + +### SUT + +Defined in [RFC2285] (section 3.1.2 System Under Test (SUT)) as follows. + +Definition: + +The collective set of network devices to which stimulus is offered +as a single entity and response measured. + +Discussion: + +An SUT consisting of a single network device is also allowed. + +### DUT + +Defined in [RFC2285] (section 3.1.1 Device Under Test (DUT)) as follows. + +Definition: + +The network forwarding device to which stimulus is offered and +response measured. + +Discussion: + +DUT, as a sub-component of SUT, is only indirectly mentioned +in MLRsearch specification, but is of key relevance for its motivation. + +{::comment} + [Could be useful, but not high priority.] + + ### Tester + + MKP3 TODO: Add Definition and Discusion paragraphs + + MKP3 MK note: Bizarre ... i can't find tester definition in + rfc1242, rfc2288 or rfc2544, but will keep looking. If there isn't one, + we need to define one :). + + [VP] TODO: There were some documents distinguishing TG and TA. + +{:/comment} + +### Trial + +A trial is the part of the test described in [RFC2544] (section 23. Trial description). + +Definition: + + A particular test consists of multiple trials. Each trial returns + one piece of information, for example the loss rate at a particular + input frame rate. Each trial consists of a number of phases: + + a) If the DUT is a router, send the routing update to the "input" + port and pause two seconds to be sure that the routing has settled. + + b) Send the "learning frames" to the "output" port and wait 2 + seconds to be sure that the learning has settled. Bridge learning + frames are frames with source addresses that are the same as the + destination addresses used by the test frames. Learning frames for + other protocols are used to prime the address resolution tables in + the DUT. The formats of the learning frame that should be used are + shown in the Test Frame Formats document. + + c) Run the test trial. + + d) Wait for two seconds for any residual frames to be received. + + e) Wait for at least five seconds for the DUT to restabilize. + +Discussion: + +The definition describes some traits, it is not clear whether all of them +are REQUIRED, or some of them are only RECOMMENDED. + +{::comment} + [Useful if possible.] + + MKP2 [VP] TODO: Search RFCs for better description of "Run the test trial". + +{:/comment} + +For the purposes of the MLRsearch specification, +it is ALLOWED for the test procedure to deviate from the [RFC2544] description, +but any such deviation MUST be made explicit in the test report. + +Trials are the only stimuli the SUT is expected to experience +during the search. + +In some discussion paragraphs, it is useful to consider the traffic +as sent and received by a tester, as implicitly defined +in [RFC2544] (section 6. Test set up). + +An example of deviation from [RFC2544] is using shorter wait times. + +## Trial Terms + +This section defines new and redefine existing terms for quantities +relevant as inputs or outputs of trial, as used by the Measurer component. + +### Trial Duration + +Definition: + +Trial duration is the intended duration of the traffic for a trial. + +Discussion: + +In general, this quantity does not include any preparation nor waiting +described in section 23 of [RFC2544] (section 23. Trial description). + +While any positive real value may be provided, some Measurer implementations +MAY limit possible values, e.g. by rounding down to neared integer in seconds. +In that case, it is RECOMMENDED to give such inputs to the Controller +so the Controller only proposes the accepted values. +Alternatively, the test report MUST present the rounded values +as Search Goal attributes. + +### Trial Load + +Definition: + +The trial load is the intended load for a trial + +Discussion: + +For test report purposes, it is assumed that this is a constant load by default. +This MAY be only an average load, e.g. when the traffic is intended to be busty, +e.g. as suggested in [RFC2544] (section 21. Bursty traffic), +but the test report MUST explicitly mention how non-constant the traffic is. + +Trial load is the quantity defined as Constant Load of [RFC1242] +(section 3.4 Constant Load), Data Rate of [RFC2544] +(section 14. Bidirectional traffic) +and Intended Load of [RFC2285] (section 3.5.1 Intended load (Iload)). +All three definitions specify +that this value applies to one (input or output) interface. + +{::comment} + [Not important.] + + MKP2 [VP] TODO: Also mention input frame rate [RFC2544] (23. Trial description). + +{:/comment} + +For test report purposes, multi-interface aggregate load MAY be reported, +this is understood as the same quantity expressed using different units. +From the report it MUST be clear whether a particular trial load value +is per one interface, or an aggregate over all interfaces. + +Similarly to trial duration, some Measurers may limit the possible values +of trial load. Contrary to trial duration, the test report is NOT REQUIRED +to document such behavior. + +{::comment} + [Can of worms. Be aware, but probably do not let spill into draft.] + + MKP2 [VP] TODO: Why? In practice the difference is small, but what if it is big? + Do we need Trial Effective Load for bounds an conditional throughput purposes? + Should the Controller be recommended to chose load values that are exactly accepted? + + +{:/comment} + +It is ALLOWED to combine trial load and trial duration in a way +that would not be possible to achieve using any integer number of data frames. + +{::comment} + [I feel this is important, to be discussed separately (not in-scope).] + + MKP2 [VP] TODO: Explain why are we not using Oload. + 1. MLRsearch implementations cannot react correctly to big differences + between Iload and Oload. + 2. The media between the tested and the DUT are thus considered to be part of SUT. + If DUT causes congestion control, it is not expected to handle Iload. + + + See further discussion in [Trial Forwarding Ratio] (#Trial-Forwarding-Ratio) + and in [Measurer] (#Measurer) sections for other related issues. + + MKP2 [VP] TODO: Create a separate subsection for Oload discussion, + or clearly separate which aspects are discussed under which term. + + MKP2 [VP] TODO: New idea. Compare the tester to an ordinary router + in some datacenter. The Intended Load is not jst some abstract input. + It is the real traffic coming from routers next hop farther. + It does not matter that DUT has forwarded each frame it received, + if the tester was unable to sent all the traffic in time. + Endpoint see packet loss, they do not care about [RFC2285] + half-duplex, spanning trees, nor congestion control mechanisms. + Formally speaking, I consider even the sending interface of the sender + to be the part of SUT. + Reading [RFC2285] (section 3.5.3 Maximum offered load (MOL)) + "This will be the case when an external source lacks the resources + to transmit frames at the minimum legal inter-frame gap" + that means TRex workers are also part of SUT. If they do not have + enough CPU power to generate frames are required, those frames are lost. + + + MKP2 [VP] TODO: That new idea warants some discussion in "DUT within SUT", + as it is just another case of ther rest of SUT ruining + otherwise good DUT performance. + +{:/comment} + +### Trial Input + +Definition: + +Trial Input is a composite quantity, consisting of two attributes: +trial duration and trial load. + +Discussion: + +When talking about multiple trials, it is common to say "Trial Inputs" +to denote all corresponding Trial Input instances. + +A Trial Input instance acts as the input for one call of the Measurer component. + +Contrary to other composite quantities, MLRsearch implementations +are NOT ALLOWED to add optional attributes here. +This improves interoperability between various implementations of +the Controller and the Measurer. + +### Traffic Profile + +Definition: + +Traffic profile is a composite quantity +containing attributes other than trial load and trial duration, +needed for unique determination of the trial to be performed. + +Discussion: + +All its attributes are assumed to be constant during the search, +and the composite is configured on the Measurer by the Manager +before the search starts. +This is why the traffic profile is not part of the Trial Input. + +As a consequence, implementations of the Manager and the Measurer +must be aware of their common set of capabilities, so that the traffic profile +uniquely defines the traffic during the search. +The important fact is that none of those capabilities +have to be known by the Controller implementations. + +The traffic profile SHOULD contain some specific quantities, +for example [RFC2544] (section 9. Frame sizes) governs +data link frame size as defined in [RFC1242] (section 3.5 Data link frame size). + +Several more specific quantities may be RECOMMENDED, depending on media type. +For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, +as recommended from [RFC2544] (section 8. Frame formats) +and [RFC2544] (section 12. Protocol addresses). + +Depending on SUT configuration, e.g. when testing specific protocols, +additional attributes MUST be included in the traffic profile +and in the test report. + +Example: [RFC8219] (section 5.3. Traffic Setup) introduces traffic setups +consisting of a mix of IPv4 and IPv6 traffic - the implied traffic profile +therefore must include an attribute for their percentage. + +Other traffic properties that need to be somehow specified +in Traffic Profile include: +[RFC2544] (section 14. Bidirectional traffic), +[RFC2285] (section 3.3.3 Fully meshed traffic), +and [RFC2544] (section 11. Modifiers). + +### Trial Forwarding Ratio + +Definition: + +The trial forwarding ratio is a dimensionless floating point value. +It MUST range between 0.0 and 1.0, both inclusive. +It is calculated by dividing the number of frames +successfully forwarded by the SUT +by the total number of frames expected to be forwarded during the trial + +Discussion: + +For most traffic profiles, "expected to be forwarded" means +"intended to get transmitted from Tester towards SUT". + +Trial forwarding ratio MAY be expressed in other units +(e.g. as a percentage) in the test report. + +Note that, contrary to loads, frame counts used to compute +trial forwarding ratio are aggregates over all SUT output interfaces. + +Questions around what is the correct number of frames +that should have been forwarded +is generally outside of the scope of this document. + +{::comment} + [Part two of iload/oload discussion.] + + See discussion in [Measurer] (#Measurer) section + for more details about calibrating test equipment. + + MKP2 [VP] TODO: Define unsent frames? + + MKP2 [VP] TODO: If Oload is fairly below Iload, the unsent frames + should be counted as lost, otherwise search outputs are misleading. + But what is "fairly"? CSIT tolerates 10 microseconds worth of unsent frames. + +{:/comment} + +{::comment} + [Low priority, but maybe useful for somebody?] + + MKP2 [VP] TODO: Mention traffic profiles with uneven frame counts? + E.g. when SUT is expected to perform IP packet fragmentation or reassembly. + + +{:/comment} + +### Trial Loss Ratio + +Definition: + +The Trial Loss Ratio is equal to one minus the trial forwarding ratio. + +Discussion: + +100% minus the trial forwarding ratio, when expressed as a percentage. + +This is almost identical to Frame Loss Rate of [RFC1242] +(section 3.6 Frame Loss Rate), +the only minor difference is that Trial Loss Ratio +does not need to be expressed as a percentage. + +### Trial Forwarding Rate + +Definition: + +The trial forwarding rate is a derived quantity, calculated by +multiplying the trial load by the trial forwarding ratio. + +Discussion: + +It is important to note that while similar, this quantity is not identical +to the Forwarding Rate as defined in [RFC2285] +(section 3.6.1 Forwarding rate (FR)). +The latter is specific to one output interface only, +whereas the trial forwarding ratio is based +on frame counts aggregated over all SUT output interfaces. + +{::comment} + [Part 3 of iload/oload discussion.] + + MKP2 [VP] TODO: If some unsent frames were tolerated (not counted as lost), + this value is actually higher than the real fps output of the SUT. + Should we use the real FR as the basis for Conditional Throughput + (instead of this TFR)? That would require additional Trial Output attribute. + + + MKP2 [VP] TODO: What about duration stretching? + This also causes difference between Iload and Oload, + but in an invisible way. + + MKP2 [VP] TODO: Recommend start+sleep+stop? + How long wait for late frames? RFC2544 2s is too much even at 30s trial. + +{:/comment} + +### Trial Effective Duration + +Definition: + +Trial effective duration is a time quantity related to the trial, +by default equal to the trial duration. + +Discussion: + +This is an optional feature. +If the Measurer does not return any trial effective duration value, +the Controller MUST use the trial duration value instead. + +Trial effective duration may be any time quantity chosen by the Measurer +to be used for time-based decisions in the Controller. + +The test report MUST explain how the Measurer computes the returned +trial effective duration values, if they are not always +equal to the trial duration. + +This feature can be beneficial for users +who wish to manage the overall search duration, +rather than solely the traffic portion of it. +Simply measure the duration of the whole trial (waits including) +and use that as the trial effective duration. + +Also, this is a way for the Measurer to inform the Controller about +its surprising behavior, for example when rounding the trial duration value. + +{::comment} + [Not very important, but easy and nice recommendation.] + + MKP2 [VP] TODO: Recommend for Measurer to return all trials at relevant bounds, + as that may better inform users when surprisingly small amount of trials + was performed, just because the the trial effective duration values were big. + + MKP2 [VP] TODO: Repeat that this is not here to deal with duration stretching. + +{:/comment} + +### Trial Output + +Definition: + +Trial Output is a composite quantity. The REQUIRED attributes are +Trial Loss Ratio, trial effective duration and trial forwarding rate. + +Discussion: + +When talking about multiple trials, it is common to say "Trial Outputs" +to denote all corresponding Trial Output instances. + +Implementations may provide additional (optional) attributes. +The Controller implementations MUST ignore values of any optional attribute +they are not familiar with, +except when passing Trial Output instance to the Manager. + +Example of an optional attribute: +The aggregate number of frames expected to be forwarded during the trial, +especially if it is not just (a rounded-up value) +implied by trial load and trial duration. + +While [RFC2285] (Section 3.5.2 Offered load (Oload)) +requires the offered load value to be reported for forwarding rate measurements, +it is NOT REQUIRED in MLRsearch specification. + +{::comment} + [Side tangent from iload/oload discussion. Stilll recommendation is not obvious.] + + MKP2 TODO: Why? Just because bound trial results are optional in Controller Output? + + MKP2 mk edit note: we need to more explicitly address + the relevance or irrelevance of [RFC2285] (Section 3.5.2 Offered load (Oload)). + Current text in [Trial Load] (#Trial-Load) is ambiguous - quoted below. + + MKP2 "Questions around what is the correct number of frames that should + have been forwarded is generally outside of the scope of this document. + See discussion in [Measurer] (#Measurer) section for more details about + calibrating test equipment." + +{:/comment} + +### Trial Result + +Definition: + +Trial result is a composite quantity, +consisting of the Trial Input and the Trial Output. + +Discussion: + +When talking about multiple trials, it is common to say "trial results" +to denote all corresponding trial result instances. + +While implementations SHOULD NOT include additional attributes +with independent values, they MAY include derived quantities. + +## Goal Terms + +This section defines new and redefine existing terms for quantities +indirectly relevant for inputs or outputs of the Controller component. + +Several goal attributes are defined before introducing +the main component quantity: the Search Goal. + +### Goal Final Trial Duration + +Definition: + +A threshold value for trial durations. + +Discussion: + +This attribute value MUST be positive. + +A trial with Trial Duration at least as long as the Goal Final Trial Duration +is called a full-length trial (with respect to the given Search Goal). + +A trial that is not full-length is called a short trial. + +Informally, while MLRsearch is allowed to perform short trials, +the results from such short trials have only limited impact on search results. + +One trial may be full-length for some Search Goals, but not for others. + +The full relation of this goal to Controller Output is defined later in +this document in subsections of [Goal Result] (#Goal-Result). +For example, the Conditional Throughput for this goal is computed only from +full-length trial results. + +### Goal Duration Sum + +Definition: + +A threshold value for a particular sum of trial effective durations. + +Discussion: + +This attribute value MUST be positive. + +Informally, even when looking only at full-length trials, +MLRsearch may spend up to this time measuring the same load value. + +If the Goal Duration Sum is larger than the Goal Final Trial Duration, +multiple full-length trials may need to be performed at the same load. + +See [TST009 Example] (#TST009-Example) for an example where possibility +of multiple full-length trials at the same load is intended. + +A Goal Duration Sum value lower than the Goal Final Trial Duration +(of the same goal) could save some search time, but is NOT RECOMMENDED. +See [Relevant Upper Bound] (#Relevant-Upper-Bound) for partial explanation. + +### Goal Loss Ratio + +Definition: + +A threshold value for Trial Loss Ratios. + +Discussion: + +Attribute value MUST be non-negative and smaller than one. + +A trial with Trial Loss Ratio larger than a Goal Loss Ratio value +is called a lossy trial, with respect to given Search Goal. + +Informally, if a load causes too many lossy trials, +the Relevant Lower Bound for this goal will be smaller than that load. + +If a trial is not lossy, it is called a low-loss trial, +or (specifically for zero Goal Loss Ratio value) zero-loss trial. + +### Goal Exceed Ratio + +Definition: + +A threshold value for a particular ratio of sums of Trial Effective Durations. + +Discussion: + +Attribute value MUST be non-negative and smaller than one. + +See later sections for details on which sums. +Specifically, the direct usage is only in +[Appendix A: Load Classification] (#Appendix-A\:-Load-Classification) +and [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput). +The impact of that usage is discussed in subsections leading to +[Goal Result] (#Goal-Result). + +Informally, the impact of lossy trials is controlled by this value. +Effectively, Goal Exceed Ratio is a percentage of full-length trials +that may be lossy without the load being classified +as the [Relevant Upper Bound] (#Relevant-Upper-Bound). + +### Goal Width + +Definition: + +A value used as a threshold for deciding +whether two trial load values are close enough. + +Discussion: + +If present, the value MUST be positive. + +Informally, this acts as a stopping condition, +controlling the precision of the search. +The search stops if every goal has reached its precision. + +Implementations without this attribute +MUST give the Controller other ways to control the search stopping conditions. + +Absolute load difference and relative load difference are two popular choices, +but implementations may choose a different way to specify width. + +The test report MUST make it clear what specific quantity is used as Goal Width. + +It is RECOMMENDED to set the Goal Width (as relative difference) value +to a value no smaller than the Goal Loss Ratio. +(The reason is not obvious, see [Throughput] (#Throughput) if interested.) + +### Search Goal + +Definition: + +The Search Goal is a composite quantity consisting of several attributes, +some of them are required. + +Required attributes: +- Goal Final Trial Duration +- Goal Duration Sum +- Goal Loss Ratio +- Goal Exceed Ratio + +Optional attribute: +- Goal Width + +Discussion: + +Implementations MAY add their own attributes. +Those additional attributes may be required by the implementation +even if they are not required by MLRsearch specification. +But it is RECOMMENDED for those implementations +to support missing values by computing reasonable defaults. + +The meaning of listed attributes is formally given only by their indirect effect +on the search results. + +Informally, later sections provide additional intuitions and examples +of the Search Goal attribute values. + +An example of additional attributes required by some implementations +is Goal Initial Trial Duration, together with another attribute +that controls possible intermediate Trial Duration values. +The reasonable default in this case is using the Goal Final Trial Duration +and no intermediate values. + +### Controller Input + +Definition: + +Controller Input is a composite quantity +required as an input for the Controller. +The only REQUIRED attribute is a list of Search Goal instances. + +Discussion: + +MLRsearch implementations MAY use additional attributes. +Those additional attributes may be required by the implementation +even if they are not required by MLRsearch specification. + +Formally, the Manager does not apply any Controller configuration +apart from one Controller Input instance. + +For example, Traffic Profile is configured on the Measurer by the Manager +(without explicit assistance of the Controller). + +The order of Search Goal instances in a list SHOULD NOT +have a big impact on Controller Output (see section [Controller Output] (#Controller-Output) , +but MLRsearch implementations MAY base their behavior on the order +of Search Goal instances in a list. + +An example of an optional attribute (outside the list of Search Goals) +required by some implementations is Max Load. +While this is a frequently used configuration parameter, +already governed by [RFC2544] (section 20. Maximum frame rate) +and [RFC2285] (3.5.3 Maximum offered load (MOL)), +some implementations may detect or discover it instead. + +{::comment} + [Not important directly, may matter for iload/oload.] + + MKP2 [VP] TODO: 2544 and 2285 care about half-duplex media. Should we? + +{:/comment} + +{::comment} + [Maybe obvious but I think useful. RFC2544 talks about header compression in WANs.] + + MKP2 [VP] TODO: Mention that Max Load should care about all media within SUT, + including DUT-DUT links. Important when that link carries encapsulated traffic, + as bandwidth limit there implies lower max rate + (than implied by tester-SUT links). + +{:/comment} + +In MLRsearch specification, the [Relevant Upper Bound] (#Relevant-Upper-Bound) +is added as a required attribute precisely because it makes the search result +independent of Max Load value. + +{::comment} + [User recommendation, we should have separate section summarizing those.] + + [VP] TODO for MK: The rest of this subsection is new, review? + + It is RECOMMENDED to use the same Goal Final Trial Duration value across all goals. + Otherwise, some goals may be measured at Trial Durations longer than needed, + with possibly unexpected impacts on repeatability and comparability. + + For example when Goal Loss Ratio is zero, any increase in Trial Duration + also increases the likelihood of the trial to become lossy, + similar to usage of lower Goal Exceed Ratio or larger Goal Duration Sum, + both of which tend to lower the search results, towards noisy end + of performance spectrum. + + Also, it is recommended to avoid "incomparable" goals, e.g. one with + lower loss ratio but higher exceed ratio, and other with higher loss ratio + but lower loss ratio. In worst case, this can make the search to last too long. + Implementations are RECOMMENDED to sort the goals and start with + stricter ones first, as bounds for those will not get invalidated + byt measureing for less trict goal later in the search. + +{:/comment} + +## Search Goal Examples + +### RFC2544 Goal + +The following set of values makes the search result unconditionally compliant +with [RFC2544] (section 24 Trial duration) + +- Goal Final Trial Duration = 60 seconds +- Goal Duration Sum = 60 seconds +- Goal Loss Ratio = 0% +- Goal Exceed Ratio = 0% + +The latter two attributes are enough to make the search goal +conditionally compliant, adding the first attribute +makes it unconditionally compliant. + +The second attribute (Goal Duration Sum) only prevents MLRsearch +from repeating zero-loss full-length trials. + +Non-zero exceed ratio could prolong the search and allow loss inversion +between lower-load lossy short trial and higher-load full-length zero-loss trial. +From [RFC2544] alone, it is not clear whether that higher load +could be considered as compliant throughput. + +### TST009 Goal + +One of the alternatives to RFC2544 is described in +[TST009] (section 12.3.3 Binary search with loss verification). +The idea there is to repeat lossy trials, hoping for zero loss on second try, +so the results are closer to the noiseless end of performance sprectum, +and more repeatable and comparable. + +Only the variant with "z = infinity" is achievable with MLRsearch. + +{::comment} + [Low priority, unless a short sentence is found.] + + MKP2 MK note: Shouldn't we add a note about how MLRsearch goes about + addressing the TST009 point related to z, that is "z is threshold of + Lord(r) to override Loss Verification when the count of lost frames is + very high and unnecessary verification trials."? i.e. by have Goal Loss + Ratio. Thoughts? + +{:/comment} + +For example, for "r = 2" variant, the following search goal should be used: + +- Goal Final Trial Duration = 60 seconds +- Goal Duration Sum = 120 seconds +- Goal Loss Ratio = 0% +- Goal Exceed Ratio = 50% + +If the first 60s trial has zero loss, it is enough for MLRsearch to stop +measuring at that load, as even a second lossy trial +would still fit within the exceed ratio. + +But if the first trial is lossy, MLRsearch needs to perform also +the second trial to classify that load. +As Goal Duration Sum is twice as long as Goal Final Trial Duration, +third full-length trial is never needed. + +## Result Terms + +Before defining the output of the Controller, +it is useful to define what the Goal Result is. + +The Goal Result is a composite quantity. + +Following subsections define its attribute first, before describing the Goal Result quantity. + +There is a correspondence between Search Goals and Goal Results. +Most of the following subsections refer to a given Search Goal, +when defining attributes of the Goal Result. +Conversely, at the end of the search, each Search Goal +has its corresponding Goal Result. + +Conceptually, the search can be seen as a process of load classification, +where the Controller attempts to classify some loads as an Upper Bound +or a Lower Bound with respect to some Search Goal. + +Before defining real attributes of the goal result, +it is useful to define bounds in general. + +### Relevant Upper Bound + +Definition: + +The Relevant Upper Bound is the smallest trial load value that is classified +at the end of the search as an upper bound +(see [Appendix A: Load Classification] (#Appendix-A\:-Load-Classification)) +for the given Search Goal. + +Discussion: + +One search goal can have many different load classified as an upper bound. +At the end of the search, one of those loads will be the smallest, +becoming the relevant upper bound for that goal. + +In more detail, the set of all trial outputs (both short and full-length, +enough of them according to Goal Duration Sum) +performed at that smallest load failed to uphold all the requirements +of the given Search Goal, mainly the Goal Loss Ratio +in combination with the Goal Exceed Ratio. + +{::comment} + [Recheck. Move to end?] + + [VP] TODO: Is the above a good summary of Appendix A inputs? + +{:/comment} + +If Max Load does not cause enough lossy trials, +the Relevant Upper Bound does not exist. +Conversely, if Relevant Upper Bound exists, +it is not affected by Max Load value. + +{::comment} + [Medium priority, depends on how many user recommendations we have.] + + With non-zero exceed ratio values, a lossy short trial may not be enough + to classify a load as the relevant upper bound. + Users MAY apply Goal Duration Sum value lower than Goal Final Trial Duration + to force such classification in hope to save time, + but it is RECOMMENDED not to do so, as in practice + it hurts comparability and repeatability. + +{:/comment} + +{::comment} + [Probably too technical, unless relation to repeatability is found.] + + In general, a load starts as as undecided, then maybe flips to become + an upper bound. MLRsearch stops measuring at that load for this goal, + but it may be forced to measure more for some other search goals, + in which case the load may flip to a lower bound (and back and forth). + + [VP] TODO: Confirm the load can never flip back to being undecided. + + Even though the load classification may change during the search, + the goal results are established at the end of the search. + + If the exceed ratio is zero, an upper bound can never flip; + one lossy trial (even short) is enough to pin the classification. + +{:/comment} + +### Relevant Lower Bound + +Definition: + +The Relevant Lower Bound is the largest trial load value +among those smaller than the Relevant Upper Bound, +that got classified at the end of the search as a lower bound (see +[Appendix A: Load Classification] (#Appendix-A\:-Load-Classification)) +for the given Search Goal. + +Discussion: + +Only among loads smaller that the relevant upper bound, +the largest load becomes the relevant lower bound. +With loss inversion, stricter upper bound matters. + +In more detail, the set of all trial outputs (both short and full-length, +enough of them according to Goal Duration Sum) +performed at that largest load managed to uphold all the requirements +of the given Search Goal, mainly the Goal Loss Ratio +in combination with the Goal Exceed Ratio. + +Is no load had enough low-loss trials, the relevant lower bound +MAY not exist. + +{::comment} + [Min Load us useful for detecting broken SUTs (and latency).] + + [VP] TODO: Mention min load here? + + [VP] TODO: Allow zero as implicit lower bound that needs no trials? + If yes, then probably way earlier than here. + +{:/comment} + +Strictly speaking, if the Relevant Upper Bound does not exist, +the Relevant Lower Bound also does not exist. +In that case, Max Load is classified as a lower bound, +but it is not clear whether a higher lower bound +would be found if the search used a higher Max Load value. + +For a regular Goal Result, the distance between the Relevant Lower Bound +and the Relevant Upper Bound MUST NOT be larger than the Goal Width, +if the implementation offers width as a goal attribute. + +{::comment} + [True but no time to fix properly.] + + mk note: Seemingly broken grammar, + "managed to uphold all requirements", should be followed + by stating what it means. + +{:/comment} + +Searching for anther search goal may cause a loss inversion phenomenon, +where a lower load is classified as an upper bound, +but also a higher load is classified as a lower bound for the same search goal. +The definition of the Relevant Lower Bound ignores such high lower bounds. + +{::comment} + [Compare to similar block in upper bound.] + + In general, a load starts as as undecided, then maybe flips to become + a lower bound. MLRsearch stops measuring at that load for this goal, + but it may be forced to measure more for some other search goals, + in which case the load may flip to an upper bound (and back and forth). + + [VP] TODO: Confirm the load can never flip back to being undecided. + + Even though the load classification may change during the search, + the goal results are established at the end of the search. + + No valid exceed ratio value pins the classification as a lower bound. + +{:/comment} + +### Conditional Throughput + +Definition: + +The Conditional Throughput (see section [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput)) +as evaluated at the Relevant Lower Bound of the given Search Goal +at the end of the search. + +Discussion: + +Informally, this is a typical trial forwarding rate, expected to be seen +at the Relevant Lower Bound of the given Search Goal. + +But frequently it is only a conservative estimate thereof, +as MLRsearch implementations tend to stop gathering more data +as soon as they confirm the value cannot get worse than this estimate +within the Goal Duration Sum. + +This value is RECOMMENDED to be used when evaluating repeatability +and comparability if different MLRsearch implementations. + +{::comment} + [Low priority but useful for comparabuility.] + + [VP] TODO: Add subsection for Trial Results At Relevant Bounds + as an optional attribute of Goal Result. + +{:/comment} + +### Goal Result + +Definition: + +The Goal Result is a composite quantity consisting of several attributes. +Relevant Upper Bound and Relevant Lower Bound are REQUIRED attributes, +Conditional Throughput is a RECOMMENDED attribute. + +Discussion: + +Depending on SUT behavior, it is possible that one or both relevant bounds +do not exist. The goal result instance where the required attribute values exist +is informally called a Regular Goal Result instance, +so we can say some goals reached Irregular Goal Results. + +{::comment} + [Probably delete after last edits re irregular results.] + + MKP2 [VP] TODO: Additional attributes should not be required by the Manager? + Explicitly mention that irregular goal result may support different attributes. + + + MKP2 Implementations are free to define their own specific subtypes + of irregular Goal Results, but the test report MUST mark them clearly + as irregular according to this section. + +{:/comment} + +A typical Irregular Goal Result is when all trials at the Max Load +have zero loss, as the Relevant Upper Bound does not exist in that case. + +It is RECOMMENDED that the test report will display such results appropriately, +although MLRsearch specification does not prescibe how. + +{::comment} + [Useful.] + + MKP2 [VP] TODO: Also allways-fail. Link to bounds to avoid duplication. + +{:/comment} + +Anything else regarging Irregular Goal Results, +including their role in stopping conditions of the search +is outside the scope of this document. + +### Search Result + +Definition: + +The Search Result is a single composite object +that maps each Search Goal instance to a corresponding Goal Result instance. + +Discussion: + +Alternatively, the Search Result can be implemented as an ordered list +of the Goal Result instances, matching the order of Search Goal instances. + +{::comment} + [Low priority, as there is no obvious harm.] + + MKP1 [VP] TODO: Disallow any additional attributes? + +{:/comment} + +The Search Result (as a mapping) +MUST map from all the Search Goal instances present in the Controller Input. + +{::comment} + [Not important.] + + [VP] Postponed: API independence, modularity. + +{:/comment} + +{::comment} + [Not needed?] + + MKP1 [VP] TODO: Short sentence on what to do on irregular goal result. + +{:/comment} + +### Controller Output + +Definition: + +The Controller Output is a composite quantity returned from the Controller +to the Manager at the end of the search. +The Search Result instance is its only REQUIRED attribute. + +Discussion: + +MLRsearch implementation MAY return additional data in the Controller Output. + +{::comment} + [Not needed?] + + MKP1 [VP] TODO: Short sentence on what to do on irregular goal result. + + MKP1 [VP] TODO: Irregular output, e.g. with "max search time exceeded" flag? + +{:/comment} + +## MLRsearch Architecture + +{::comment} + [Meta and irrelevant. Delete after verifying other text is good.] + + MKP2 [VP] TODO: Review the folowing: + This section is about division into components, + so it fits this definition: + "The software architecture of a system represents the design decisions + related to overall system structure and behavior." + Saying "MLRsearch Design" does not make it clear if it is + Vratko designing the MLRsearch specification, + or some other person designing a new MLRsearch implementation using that spec. + + +{:/comment} + +MLRsearch architecture consists of three main system components: +the Manager, the Controller, and the Measurer. + +The architecture also implies the presence of other components, +such as the SUT and the Tester (as a sub-component of the Measurer). + +Protocols of communication between components are generally left unspecified. +For example, when MLRsearch specification mentions "Controller calls Measurer", +it is possible that the Controller notifies the Manager +to call the Measurer indirectly instead. This way the Measurer implementations +can be fully independent from the Controller implementations, +e.g. programmed in different programming languages. + +### Measurer + +Definition: + +The Measurer is an abstract system component +that when called with a [Trial Input] (#Trial-Input) instance, +performs one [Trial] (#Trial), +and returns a [Trial Output] (#Trial-Output) instance. + +Discussion: + +This definition assumes the Measurer is already initialized. +In practice, there may be additional steps before the search, +e.g. when the Manager configures the traffic profile +(either on the Measurer or on its tester sub-component directly) +and performs a warmup (if the tester requires one). + +It is the responsibility of the Measurer implementation to uphold +any requirements and assumptions present in MLRsearch specification, +e.g. trial forwarding ratio not being larger than one. + +Implementers have some freedom. +For example [RFC2544] (section 10. Verifying received frames) +gives some suggestions (but not requirements) related to +duplicated or reordered frames. +Implementations are RECOMMENDED to document their behavior +related to such freedoms in as detailed a way as possible. + +It is RECOMMENDED to benchmark the test equipment first, +e.g. connect sender and receiver directly (without any SUT in the path), +find a load value that guarantees the offered load is not too far +from the intended load, and use that value as the Max Load value. +When testing the real SUT, it is RECOMMENDED to turn any big difference +between the intended load and the offered load into increased Trial Loss Ratio. + +Neither of the two recommendations are made into requirements, +because it is not easy to tell when the difference is big enough, +in a way thay would be dis-entangled from other Measurer freedoms. + +### Controller + +Definition: + +The Controller is an abstract system component +that when called with a Controller Input instance +repeatedly computes Trial Input instance for the Measurer, +obtains corresponding Trial Output instances, +and eventually returns a Controller Output instance. + +Discussion: + +Informally, the Controller has big freedom in selection of Trial Inputs, +and the implementations want to achieve the Search Goals +in the shortest expected time. + +The Controller's role in optimizing the overall search time +distinguishes MLRsearch algorithms from simpler search procedures. + +Informally, each implementation can have different stopping conditions. +Goal Width is only one example. +In practice, implementation details do not matter, +as long as Goal Results are regular. + +### Manager + +Definition: + +The Manager is an abstract system component that is reponsible for +configuring other components, calling the Controller component once, +and for creating the test report following the reporting format as +defined in [RFC2544] (section 26. Benchmarking tests). + +Discussion: + +The Manager initializes the SUT, the Measurer (and the Tester if independent) +with their intended configurations before calling the Controller. + +The Manager does not need to be able to tweak any Search Goal attributes, +but it MUST report all applied attribute values even if not tweaked. + +{::comment} + [Not very important but also should be easy to add.] + + MKP2 [VP] TODO: Is saying "RFC2544" indirectly reporting RFC2544 Goal values? + +{:/comment} + +In principle, there should be a "user" (human or CI) +that "starts" or "calls" the Manager and receives the report. +The Manager MAY be able to be called more than once whis way. + +{::comment} + [Not important, unless anybody else asks.] + + MKP2 The Manager may use the Measurer or other system components + to perform other tests, e.g. back-to-back frames, + as the Controller is only replacing the search from + [RFC2544] (section 26.1 Throughput). + +{:/comment} + +## Implementation Compliance + +Any networking measurement setup where there can be logically delineated system components +and there are components satisfying requirements for the Measurer, +the Controller and the Manager, is considered to be compliant with MLRsearch design. + +These components can be seen as abstractions present in any testing procedure. +For example, there can be a single component acting both +as the Manager and the Controller, but as long as values of required attributes +of Search Goals and Goal Results are visible in the test report, +the Controller Input instance and output instance are implied. + +For example, any setup for conditionally (or unconditionally) +compliant [RFC2544] throughput testing +can be understood as a MLRsearch architecture, +assuming there is enough data to reconstruct the Relevant Upper Bound. + +See [RFC2544 Goal] (#RFC2544-Goal) subsection for equivalent Search Goal. + +Any test procedure that can be understood as (one call to the Manager of) +MLRsearch architecture is said to be compliant with MLRsearch specification. + +# Additional Considerations + +This section focuses on additional considerations, intuitions and motivations +pertaining to MLRsearch methodology. + +{::comment} + [Meta, redundant.] + + MKP2 [VP] TODO: Review the following: + If MLRsearch specification is a product design specification + for MLRsearch implementation, then this chapter talks about + my goals and early attempts at designing the MLRsearch specification. + + +{:/comment} + +## MLRsearch Versions + +The MLRsearch algorithm has been developed in a code-first approach, +a Python library has been created, debugged, used in production +and published in PyPI before the first descriptions +(even informal) were published. + +But the code (and hence the description) was evolving over time. +Multiple versions of the library were used over past several years, +and later code was usually not compatible with earlier descriptions. + +The code in (some version of) MLRsearch library fully determines +the search process (for a given set of configuration parameters), +leaving no space for deviations. + +{::comment} + [Different type of external link, should be in 08.] + + MKP2 mk3 note: any references to library + should have specific reference link. + We have FDio-CSIT-MLRsearch in informative: at the start. Link it. + + +{:/comment} + +{::comment} + [Lesson learned is important, but maybe does not need version history?] + + MKP2 mk edit note: Suggest to remove crossed-out text, as it is + distracting, doesn't bring any value, and recalls multiple versions of + MLRsearch library, without any references. A much more appropriate + approach would be to provide a pointer to MLRsearch code versions in + FD.io that evolved over the years, as an example implementation. But I + would question the value of referring to old previous versions in this + document. It's okay for the blog, but not for IETF specification, + unless there are specific lessons learned that need to be highlighted + to support the specification. + +{:/comment} + +This historic meaning of MLRsearch, as a family +of search algorithm implementations, +leaves plenty of space for future improvements, at the cost +of poor comparability of results of search algoritm implementations. + +{::comment} + [Reckeck after clarifying library/algorithm/implementation/specification mess.] + + mk edit note: If the aim of this sentence is to state that there + could be possibly other approaches to address this problem space, then + I think we are already addressing it in the opening sections discussing + problems, and referring to ETSi TST.009 and opnfv work. If the aim is + to define "MLRsearch" as a completely new class of algorithms for + software network benchmarking, of which this spec is just one example, + then i have a problem with it. This specification is very prescriptive + in the main functional areas to address the problem identified, but + still leaving space for further exploration and innovation as noted + elsewhere in this document. It is not a new class of algorithms. It is + a newly defined methodology to amend RFC2544, to specifically address + identified problems. + +{:/comment} + +There are two competing needs. +There is the need for standardization in areas critical to comparability. +There is also the need to allow flexibility for implementations +to innovate and improve in other areas. +This document defines MLRsearch as a new specification +in a manner that aims to fairly balance both needs. + +## Stopping Conditions + +[RFC2544] prescribes that after performing one trial at a specific offered load, +the next offered load should be larger or smaller, based on frame loss. + +The usual implementation uses binary search. +Here a lossy trial becomes +a new upper bound, a lossless trial becomes a new lower bound. +The span of values between the tightest lower bound +and the tightest upper bound (including both values) forms an interval of possible results, +and after each trial the width of that interval halves. + +Usually the binary search implementation tracks only the two tightest bounds, +simply calling them bounds. +But the old values still remain valid bounds, +just not as tight as the new ones. + +After some number of trials, the tightest lower bound becomes the throughput. +[RFC2544] does not specify when, if ever, should the search stop. + +MLRsearch introduces a concept of [Goal Width] (#Goal-Width). + +The search stops +when the distance between the tightest upper bound and the tightest lower bound +is smaller than a user-configured value, called Goal Width from now on. +In other words, the interval width at the end of the search +has to be no larger than the Goal Width. + +This Goal Width value therefore determines the precision of the result. +Due to the fact that MLRsearch specification requires a particular +structure of the result (see [Trial Result] (#Trial-Result) section), +the result itself does contain enough information to determine its +precision, thus it is not required to report the Goal Width value. + +This allows MLRsearch implementations to use stopping conditions +different from Goal Width. + +## Load Classification + +MLRsearch keeps the basic logic of binary search (tracking tightest bounds, +measuring at the middle), perhaps with minor technical differences. + +MLRsearch algorithm chooses an intended load (as opposed to the offered load), +the interval between bounds does not need to be split +exactly into two equal halves, +and the final reported structure specifies both bounds. + +The biggest difference is that to classify a load +as an upper or lower bound, MLRsearch may need more than one trial +(depending on configuration options) to be performed at the same intended load. + +In consequence, even if a load already does have few trial results, +it still may be classified as undecided, neither a lower bound nor an upper bound. + +An explanation of the classification logic is given in the next section [Logic of Load Classification] (#Logic-of-Load-Classification), +as it heavily relies on other subsections of this section. + +For repeatability and comparability reasons, it is important that +given a set of trial results, all implementations of MLRsearch +classify the load equivalently. + +## Loss Ratios + +Another difference between MLRsearch and [RFC2544] binary search is in the goals of the search. +[RFC2544] has a single goal, +based on classifying full-length trials as either lossless or lossy. + +MLRsearch, as the name suggests, can search for multiple goals, +differing in their loss ratios. +The precise definition of the Goal Loss Ratio will be given later. +The [RFC2544] throughput goal then simply becomes a zero Goal Loss Ratio. +Different goals also may have different Goal Widths. + +A set of trial results for one specific intended load value +can classify the load as an upper bound for some goals, but a lower bound +for some other goals, and undecided for the rest of the goals. + +Therefore, the load classification depends not only on trial results, +but also on the goal. +The overall search procedure becomes more complicated, when +compared to binary search with a single goal, +but most of the complications do not affect the final result, +except for one phenomenon, loss inversion. + +## Loss Inversion + +In [RFC2544] throughput search using bisection, any load with a lossy trial +becomes a hard upper bound, meaning every subsequent trial has a smaller +intended load. + +But in MLRsearch, a load that is classified as an upper bound for one goal +may still be a lower bound for another goal, and due to the other goal +MLRsearch will probably perform trials at even higher loads. +What to do when all such higher load trials happen to have zero loss? +Does it mean the earlier upper bound was not real? +Does it mean the later lossless trials are not considered a lower bound? +Surely we do not want to have an upper bound at a load smaller than a lower bound. + +MLRsearch is conservative in these situations. +The upper bound is considered real, and the lossless trials at higher loads +are considered to be a coincidence, at least when computing the final result. + +This is formalized using new notions, the [Relevant Upper Bound] (#Relevant-Upper-Bound) and +the [Relevant Lower Bound] (#Relevant-Lower-Bound). +Load classification is still based just on the set of trial results +at a given intended load (trials at other loads are ignored), +making it possible to have a lower load classified as an upper bound, +and a higher load classified as a lower bound (for the same goal). +The Relevant Upper Bound (for a goal) is the smallest load classified +as an upper bound. +But the Relevant Lower Bound is not simply +the largest among lower bounds. +It is the largest load among loads +that are lower bounds while also being smaller than the Relevant Upper Bound. + +With these definitions, the Relevant Lower Bound is always smaller +than the Relevant Upper Bound (if both exist), and the two relevant bounds +are used analogously as the two tightest bounds in the binary search. +When they are less than the Goal Width apart, +the relevant bounds are used in the output. + +One consequence is that every trial result can have an impact on the search result. +That means if your SUT (or your traffic generator) needs a warmup, +be sure to warm it up before starting the search. + +## Exceed Ratio + +The idea of performing multiple trials at the same load comes from +a model where some trial results (those with high loss) are affected +by infrequent effects, causing poor repeatability of [RFC2544] throughput results. +See the discussion about noiseful and noiseless ends +of the SUT performance spectrum in section [DUT in SUT] (#DUT-in-SUT). +Stable results are closer to the noiseless end of the SUT performance spectrum, +so MLRsearch may need to allow some frequency of high-loss trials +to ignore the rare but big effects near the noiseful end. + +MLRsearch can do such trial result filtering, but it needs +a configuration option to tell it how frequent can the infrequent big loss be. +This option is called the exceed ratio. +It tells MLRsearch what ratio of trials +(more exactly what ratio of trial seconds) can have a [Trial Loss Ratio] (#Trial-Loss-Ratio) +larger than the Goal Loss Ratio and still be classified as a lower bound. +Zero exceed ratio means all trials have to have a Trial Loss Ratio +equal to or smaller than the Goal Loss Ratio. + +For explainability reasons, the RECOMMENDED value for exceed ratio is 0.5, +as it simplifies some later concepts by relating them to the concept of median. + +## Duration Sum + +When more than one trial is intended to classify a load, +MLRsearch also needs something that controls the number of trials needed. +Therefore, each goal also has an attribute called duration sum. + +The meaning of a [Goal Duration Sum] (#Goal-Duration-Sum) is that +when a load has (full-length) trials +whose trial durations when summed up give a value at least as big +as the Goal Duration Sum value, +the load is guaranteed to be classified either as an upper bound +or a lower bound for that goal. + +Due to the fact that the duration sum has a big impact +on the overall search duration, and [RFC2544] prescribes +wait intervals around trial traffic, +the MLRsearch algorithm is allowed to sum durations that are different +from the actual trial traffic durations. + +In the MLRsearch specification, the different duration values are called +[Trial Effective Duration] (#Trial-Effective-Duration). + +## Short Trials + +MLRsearch requires each goal to specify its final trial duration. +Full-length trial is a shorter name for a trial whose intended trial duration +is equal to (or longer than) the goal final trial duration. + +Section 24 of [RFC2544] already anticipates possible time savings +when short trials (shorter than full-length trials) are used. +Full-length trials are the opposite of short trials, +so they may also be called long trials. + +Any MLRsearch implementation may include its own configuration options +which control when and how MLRsearch chooses to use short trial durations. + +For explainability reasons, when exceed ratio of 0.5 is used, +it is recommended for the Goal Duration Sum to be an odd multiple +of the full trial durations, so Conditional Throughput becomes identical to +a median of a particular set of trial forwarding rates. + +The presence of short trial results complicates the load classification logic. + +Full details are given later in section [Logic of Load Classification] (#Logic-of-Load-Classification). +In a nutshell, results from short trials +may cause a load to be classified as an upper bound. +This may cause loss inversion, and thus lower the Relevant Lower Bound, +below what would classification say when considering full-length trials only. + +{::comment} + [I still think this is important, revisit after explanations re quantiles.] + + For explainability reasons, it is RECOMMENDED users use such configurations + that guarantee all trials have the same length. + + mk edit note: Using RFC2119 keyword here does not seem to be + appropriate. Moreover, I do not get the meaning nor the logic behind + this statement. It seems to say that in order for users to understand + the workings of MLRsearch, they should use simplified configuration, + otherwise they won't get it. Illogical it seems to me. Suggest to + remove it. + +{:/comment} + +{::comment} + [Important. Keeping compatibility slows search considerably.] + + Alas, such configurations are usually not compliant with [RFC2544] requirements, + or not time-saving enough. + + mk edit note: This statement does not make sense to me. Suggest to remove it. + +{:/comment} + +## Throughput + +{::comment} + [Important, we need better title.] + + [VP] TODO: Was named Conditional Troughput, but spec chapter already has one. + +{:/comment} + +Due to the fact that testing equipment takes the intended load as an input parameter +for a trial measurement, any load search algorithm needs to deal +with intended load values internally. + +But in the presence of goals with a non-zero loss ratio, the intended load +usually does not match the user's intuition of what a throughput is. +The forwarding rate (as defined in [RFC2285] section 3.6.1) is better, +but it is not obvious how to generalize it +for loads with multiple trial results and a non-zero +[Goal Loss Ratio] (#Goal-Loss-Ratio). + +The best example is also the main motivation: hard limit performance. +Even if the medium allows higher performance, +the SUT interfaces may have their additional own limitations, +e.g. a specific fps limit on the NIC (a very common occurance). + +Ideally, those should be known and used when computing Max Load. +But if Max Load is higher that what interface can receive or transmit, +there will be a "hard limit" observed in trial results. +Imagine the hard limit is at 100 Mfps, Max Load is higher, +and the goal loss ratio is 0.5%. If DUT has no additional losses, +0.5% loss ratio will be achieved at 100.5025 Mfps (the relevant lower bound). +But it is not intuitive to report SUT performance as a value that is +larger than known hard limit. +We need a generalization of RFC2544 throughput, +different from just the relevant lower bound. + +MLRsearch defines one such generalization, called the Conditional Throughput. +It is the trial forwarding rate from one of the trials +performed at the load in question. +Determining which trial exactly is defined in +[MLRsearch Specification] (#MLRsearch-Specification), +and in [Appendix B: Conditional Throughput] (#Appendix-B\:-Conditional-Throughput). + +In the hard limit example, 100.5 Mfps load will still have +only 100.0 Mfps forwarding rate, nicely confirming the known limitation. + +Conditional Throughput is partially related to load classification. +If a load is classified as a lower bound for a goal, +the Conditional Throughput can be calculated from trial results, +and guaranteed to show an loss ratio +no larger than the Goal Loss Ratio. + +{::comment} + [Revisit after other edits, may be addressed elsewhere.] + + While the Conditional Throughput gives more intuitive-looking + values than the Relevant Lower Bound (for non-zero Goal Loss Ratio + values), the actual definition is more complicated than the definition + of the Relevant Lower Bound. + + mk edit note: Looking at this again, and per improved text, I + don't think it is that complicated. (BTW saying it is more complicated + and not addressing it, and leaving it open ended is not + good.) "Conditional throughput" intuitively is really throughput under + certain conditions, these being offered load determined by Relevant + Lower Bound and actual loss. For comparability, and taking into account + multiple trial samples, per MLRsearch definition, this is + mathematically expressed as `conditional_throughput = intended_load * + (1.0 - quantile_loss_ratio)`. + + DONE VP to MK: Hmm. Frequently, Conditional Throughput comes + from the worst among low-loss full-length trials. + But if two disparate goals are interested at the same load, + things get complicated (does not happen in CSIT production, + but I found few bugs when testing in simulator). + Computation in load classification is also not trivial, + but at least it only needs two "duration sum" values, + no need to sort all trial results. + + MKP2 [VP] TODO: Still not sure what to do with this subsection. + Possibly a bigger rewrite once VP and MK agree on what is (or is not) + complicated. :) + +{:/comment} + +{::comment} + [Important only for "design principles" chapter we may never have.] + + In the future, other intuitive values may become popular, + but they are unlikely to supersede the definition of the Relevant Lower Bound + as the most fitting value for comparability purposes, + therefore the Relevant Lower Bound remains a required attribute + of the Goal Result structure, while the Conditional Throughput is only optional. + + mk edit note: This paragraph adds to the confusion. I would remove + this paragraph, as with the new text above it doesn't seem to add any + value. + + [VP] TODO: This is an example of MLRsearch design principles. + +{:/comment} + +{::comment} + [Useful.] + + [VP] TODO: Mention somewhere that trending is a specific case + of repeatability/comparability. + +{:/comment} + +Note that when comparing the best (all zero loss) and worst case (all loss +just below Goal Loss Ratio), the same Relevant Lower Bound value +may result in the Conditional Throughput differing up to the Goal Loss Ratio. + +Therefore it is rarely needed to set the Goal Width (if expressed +as the relative difference of loads) below the Goal Loss Ratio. +In other words, setting the Goal Width below the Goal Loss Ratio +may cause the Conditional Throughput for a larger loss ratio to become smaller +than a Conditional Throughput for a goal with a smaller Goal Loss Ratio, +which is counter-intuitive, considering they come from the same search. +Therefore it is RECOMMENDED to set the Goal Width to a value no smaller +than the Goal Loss Ratio. + +Overall, this Conditional Throughput does behave well for comparability purposes. + +## Search Time + +MLRsearch was primarily developed to reduce the time +required to determine a throughput, either the [RFC2544] compliant one, +or some generalization thereof. +The art of achieving short search times +is mainly in the smart selection of intended loads (and intended durations) +for the next trial to perform. + +While there is an indirect impact of the load selection on the reported values, +in practice such impact tends to be small, +even for SUTs with quite a broad performance spectrum. + +A typical example of two approaches to load selection leading to different +Relevant Lower Bounds is when the interval is split in a very uneven way. +Any implementation choosing loads very close to the current Relevant Lower Bound +is quite likely to eventually stumble upon a trial result +with poor performance (due to SUT noise). +For an implementation choosing loads very close +to the current Relevant Upper Bound, this is unlikely, +as it examines more loads that can see a performance +close to the noiseless end of the SUT performance spectrum. + +However, as even splits optimize search duration at give precision, +MLRsearch implementations that prioritize minimizing search time +are unlikely to suffer from any such bias. + +Therefore, this document remains quite vague on load selection +and other optimization details, and configuration attributes related to them. +Assuming users prefer libraries that achieve short overall search time, +the definition of the Relevant Lower Bound +should be strict enough to ensure result repeatability +and comparability between different implementations, +while not restricting future implementations much. + +{::comment} + [Important for BMWG. Configurability is bad for comparability.] + + MKP2 Sadly, different implementations may exhibit their sweet spot of + the best repeatability for a given search duration + at different goals attribute values, especially concerning + any optional goal attributes such as the initial trial duration. + Thus, this document does not comment much on which configurations + are good for comparability between different implementations. + For comparability between different SUTs using the same implementation, + refer to configurations recommended by that particular implementation. + + MKP2 mk edit note: Isn't this going off on a tangent, hypothesising and + second guessing about different possible implementations. What is the + value of this content to this document? Suggest to remove it. + +{:/comment} + +## [RFC2544] Compliance + +Some Search Goal instances lead to results compliant with RFC2544. +See [RFC2544 Goal] (#RFC2544-Goal) for more details +regarding both conditional and unconditional compliance. + +The presence of other Search Goals does not affect the compliance +of this Goal Result. +The Relevant Lower Bound and the Conditional Throughput are in this case +equal to each other, and the value is the [RFC2544] throughput. + +# Logic of Load Classification + +## Introductory Remarks + +This chapter continues with explanations, +but this time more precise definitions are needed +for readers to follow the explanations. + +Descriptions in this section are wordy and implementers should read +[MLRsearch Specification] (#MLRsearch-Specification) section +and Appendices for more concise definitions. + +The two areas of focus here are load classification +and the Conditional Throughput. + +To start with [Performance Spectrum] (#Performance-Spectrum) +subsection contains definitions needed to gain insight +into what Conditional Throughput means. +Remaining subsections discuss load classification. + +For load classification, it is useful to define **good trials** and **bad trials**: + +- **Bad trial**: Trial is called bad (according to a goal) + if its [Trial Loss Ratio] (#Trial-Loss-Ratio) + is larger than the [Goal Loss Ratio] (#Goal-Loss-Ratio). + +- **Good trial**: Trial that is not bad is called good. + +## Performance Spectrum +### Description + +There are several equivalent ways to explain the Conditional Throughput +computation. One of the ways relies on performance +spectrum. + +Take an intended load value, a trial duration value, and a finite set +of trial results, with all trials measured at that load value and duration value. + +The performance spectrum is the function that maps +any non-negative real number into a sum of trial durations among all trials +in the set, that has that number, as their trial forwarding rate, +e.g. map to zero if no trial has that particular forwarding rate. + +A related function, defined if there is at least one trial in the set, +is the performance spectrum divided by the sum of the durations +of all trials in the set. + +That function is called the performance probability function, as it satisfies +all the requirements for probability mass function +of a discrete probability distribution, +the one-dimensional random variable being the trial forwarding rate. + +These functions are related to the SUT performance spectrum, +as sampled by the trials in the set. + +{::comment} + [Middle of rewrite?] + + MKP1 The performance spectrum is the function that maps + any non-negative real number into a sum of trial durations among all trials + in the set, that has that number, as their trial forwarding rate, + e.g. map to zero if no trial has that particular forwarding rate. + + MKP1 A related function, defined if there is at least one trial in the set, + is the performance spectrum divided by the sum of the durations + of all trials in the set. + + MKP1 That function is called the performance probability function, as it satisfies + all the requirements for probability mass function + of a discrete probability distribution, + the one-dimensional random variable being the trial forwarding rate. + + MKP1 These functions are related to the SUT performance spectrum, + as sampled by the trials in the set. + + MKP1 [VP] TODO: Introduce quantiles properly by incorporating the below. + + MKP1 [VP] TODO: "q-quantile" is plainly wrong. I meant the "p" in "p-quantile". + + - wikipedia: The 100-quantiles are called percentiles + - also wiki: If, instead of using integers k and q, the "p-quantile" is based on a real number p with 0 < p < 1 then... + - https://en.wikipedia.org/wiki/Quantile_function + - exceed ratio is an input to a quantile function: percentage? + + + MKP1 mk2 TODO for VP: Above is not making it clearer at all. Can't we really not explain the spectrum and exceed ratio with just percentiles and quantiles? + + As for any other probability function, we can talk about percentiles + of the performance probability function, including the median. + The Conditional Throughput will be one such quantile value + for a specifically chosen set of trials. + + MKP2 As for any other probability function, we can talk about percentiles + of the performance probability function, including the median. + The Conditional Throughput will be one such quantile value + for a specifically chosen set of trials. + +{:/comment} + +Take a set of all full-length trials performed at the Relevant Lower Bound, +sorted by decreasing trial forwarding rate. +The sum of the durations of those trials +may be less than the Goal Duration Sum, or not. +If it is less, add an imaginary trial result with zero trial forwarding rate, +such that the new sum of durations is equal to the Goal Duration Sum. +This is the set of trials to use. + +If the quantile touches two trials, + +{::comment} + [Clarity.] + + mk edit note: What does it mean "quantile touches two trials"? + Do you mean two trials are within specific quantile or percentile? + +{:/comment} + +the larger trial forwarding rate (from the trial result sorted earlier) is used. + +{::comment} + [Oh, unspecified exceed ratio?] + + the larger trial forwarding rate (from the trial result sorted earlier) is used. + + mk edit note: Why is that? Is it because you silently assumed that + quantile here is median or 50th percentile? + +{:/comment} + +The resulting quantity is the Conditional Throughput of the goal in question. + +{::comment} + [Motivation has lead to code. Now code is definition, this should be equivalent.] + + The resulting quantity is the Conditional Throughput of the goal in question. + + mk edit note: Is this is supposed to be another definition of + Conditional Throughput? If so, how does this relate to Performance + Spectrum? I suggest to either remove these unclear paragraphs above and + rely on examples below that are clear, or rework above so it fits the + flow. Cause right now it's confusion. Even more so, that + [Conditional Throughput] (#Conditional-Throughput) has been already + defined elsewhere in the document. + +{:/comment} + +A set of examples follows. + +### First Example + +- [Goal Exceed Ratio] (#Goal-Exceed-Ratio) = 0 and [Goal Duration Sum] (#Goal-Duration-Sum) has been reached. +- Conditional Throughput is the smallest trial forwarding rate among the trials. + +### Second Example + +- Goal Exceed Ratio = 0 and Goal Duration Sum has not been reached yet. +- Due to the missing duration sum, the worst case may still happen, so the Conditional Throughput is zero. +- This is not reported to the user, as this load cannot become the Relevant Lower Bound yet. + +### Third Example + +- Goal Exceed Ratio = 50% and Goal Duration Sum is two seconds. +- One trial is present with the duration of one second and zero loss. +- The imaginary trial is added with the duration of one second and zero trial forwarding rate. +- The median would touch both trials, so the Conditional Throughput is the trial forwarding rate of the one non-imaginary trial. +- As that had zero loss, the value is equal to the offered load. + +{::comment} + [Middle of rewrite?] + + MKP2 mk edit note: how is the median "touching" both trials? + Isn't median of even set of data samples + the average of the two middle data points, + in this case the non-imaginary trial and the imaginary one? + + MKP2 Note that Appendix B does not take into account short trial results. + + MKP2 mk edit note: Whis is this relevant here? Appendix B has not been mentioned in this section. + +{:/comment} + +### Summary + +While the Conditional Throughput is a generalization of the trial forwarding rate, +its definition is not an obvious one. + +Other than the trial forwarding rate, the other source of intuition +is the quantile in general, and the median the recommended case. + +{::comment} + [Next version of MLRsearch library may invent new quantity that is more stable.] + + In future, different quantities may prove more useful, + especially when applying to specific problems, + but currently the Conditional Throughput is the recommended compromise, + especially for repeatability and comparability reasons. + + MKP2 mk edit note: This is future looking and hand wavy without + specifics. What are the "specific problems" that are referred here? + Networking, else?Some specific behaviours, if so, what sort? If + something is classified as future work, it needs to be better defined. + The same applies to any out of scope statements. + +{:/comment} + +## Trials with Single Duration + +{::comment} + [Clarity.] + + MKP2 mk edit note: Need to improve explanations in this subsection. + +{:/comment} + +When goal attributes are chosen in such a way that every trial has the same +intended duration, the load classification is simpler. + +The following description follows the motivation +of Goal Loss Ratio, Goal Exceed Ratio, and Goal Duration Sum. + +If the sum of the durations of all trials (at the given load) +is less than the Goal Duration Sum, imagine two scenarios: + +- **best case scenario**: all subsequent trials having zero loss, and +- **worst case scenario**: all subsequent trials having 100% loss. + +Here we assume there are as many subsequent trials as needed +to make the sum of all trials equal to the Goal Duration Sum. + +The exceed ratio is defined using sums of durations +(and number of trials does not matter), so it does not matter whether +the "subsequent trials" can consist of an integer number of full-length trials. + +In any of the two scenarios, best case and worst case, we can compute the load exceed ratio, +as the duration sum of good trials divided by the duration sum of all trials, +in both cases including the assumed trials. + +Even if, in the best case scenario, the load exceed ratio is larger +than the Goal Exceed Ratio, the load is an upper bound. + +MKP2 Even if, in the worst case scenario, the load exceed ratio is not larger +than the Goal Exceed Ratio, the load is a lower bound. + +{::comment} + [Middle of rewrite?] + + Even if, in the best case scenario, the load exceed ratio is larger + than the Goal Exceed Ratio, the load is an upper bound. + + MKP2 Even if, in the worst case scenario, the load exceed ratio is not larger + than the Goal Exceed Ratio, the load is a lower bound. + + MKP2 mk edit note: I am confused by "Even if" prefixing + each of the above statements. And even more so by your version + with "If even". + + mk edit note: I do not get how this statements are true, as they + are counter-intuitive. For the best case scenario, if load exceed ratio + is larger than the goal exceed ratio, I expect the load to be lower + bound. Need more examples. + +{:/comment} + +More specifically: + +- Take all trials measured at a given load. +- The sum of the durations of all bad full-length trials is called the bad sum. +- The sum of the durations of all good full-length trials is called the good sum. +- The result of adding the bad sum plus the good sum is called the measured sum. +- The larger of the measured sum and the Goal Duration Sum is called the whole sum. +- The whole sum minus the measured sum is called the missing sum. +- The optimistic exceed ratio is the bad sum divided by the whole sum. +- The pessimistic exceed ratio is the bad sum plus the missing sum, that divided by the whole sum. +- If the optimistic exceed ratio is larger than the Goal Exceed Ratio, the load is classified as an upper bound. +- If the pessimistic exceed ratio is not larger than the Goal Exceed Ratio, the load is classified as a lower bound. +- Else, the load is classified as undecided. + +The definition of pessimistic exceed ratio is compatible with the logic in +the Conditional Throughput computation, so in this single trial duration case, +a load is a lower bound if and only if the Conditional Throughput +loss ratio is not larger than the Goal Loss Ratio. + +{::comment} + [Useful (depends on the whole chapter).] + + MKP2 mk edit note: I do not get the defintion of optimistic and + pessmistic exceed ratios. Please define or describe what they + are. + +{:/comment} + +If it is larger, the load is either an upper bound or undecided. + +## Trials with Short Duration + +### Scenarios + +Trials with intended duration smaller than the goal final trial duration +are called short trials. +The motivation for load classification logic in the presence of short trials +is based around a counter-factual case: What would the trial result be +if a short trial has been measured as a full-length trial instead? + +There are three main scenarios where human intuition guides +the intended behavior of load classification. + +#### False Good Scenario + +The user had their reason for not configuring a shorter goal +final trial duration. +Perhaps SUT has buffers that may get full at longer +trial durations. +Perhaps SUT shows periodic decreases in performance +the user does not want to be treated as noise. + +In any case, many good short trials may become bad full-length trials +in the counter-factual case. + +In extreme cases, there are plenty of good short trials and no bad short trials. + +In this scenario, we want the load classification NOT to classify the load +as a lower bound, despite the abundance of good short trials. + +{::comment} + [I agree.] + + MKP2 mk edit note: It may be worth adding why that is. i.e. because + there is a risk that at longer trial this could turn into a bad + trial. + +{:/comment} + +Effectively, we want the good short trials to be ignored, so they +do not contribute to comparisons with the Goal Duration Sum. + +#### True Bad Scenario + +When there is a frame loss in a short trial, +the counter-factual full-length trial is expected to lose at least as many +frames. + +In practice, bad short trials are rarely turning into +good full-length trials. + +In extreme cases, there are no good short trials. + +In this scenario, we want the load classification +to classify the load as an upper bound just based on the abundance +of short bad trials. + +Effectively, we want the bad short trials +to contribute to comparisons with the Goal Duration Sum, +so the load can be classified sooner. + +#### Balanced Scenario + +Some SUTs are quite indifferent to trial duration. +Performance probability function constructed from short trial results +is likely to be similar to the performance probability function constructed +from full-length trial results (perhaps with larger dispersion, +but without a big impact on the median quantiles overall). + +{::comment} + [Recheck after edits earlier.] + + MKP1 mk edit note: "Performance probability function" is this function + defined anywhere? Mention in [Performance Spectrum] (#Performance Spectrum) + is not a complete definition. + +{:/comment} + +For a moderate Goal Exceed Ratio value, this may mean there are both +good short trials and bad short trials. + +This scenario is there just to invalidate a simple heuristic +of always ignoring good short trials and never ignoring bad short trials, +as that simple heuristic would be too biased. + +Yes, the short bad trials +are likely to turn into full-length bad trials in the counter-factual case, +but there is no information on what would the good short trials turn into. + +The only way to decide safely is to do more trials at full length, +the same as in False Good Scenario. + +### Classification Logic + +MLRsearch picks a particular logic for load classification +in the presence of short trials, but it is still RECOMMENDED +to use configurations that imply no short trials, +so the possible inefficiencies in that logic +do not affect the result, and the result has better explainability. + +With that said, the logic differs from the single trial duration case +only in different definition of the bad sum. +The good sum is still the sum across all good full-length trials. + +Few more notions are needed for defining the new bad sum: + +- The sum of durations of all bad full-length trials is called the bad long sum. +- The sum of durations of all bad short trials is called the bad short sum. +- The sum of durations of all good short trials is called the good short sum. +- One minus the Goal Exceed Ratio is called the subceed ratio. +- The Goal Exceed Ratio divided by the subceed ratio is called the exceed coefficient. +- The good short sum multiplied by the exceed coefficient is called the balancing sum. +- The bad short sum minus the balancing sum is called the excess sum. +- If the excess sum is negative, the bad sum is equal to the bad long sum. +- Otherwise, the bad sum is equal to the bad long sum plus the excess sum. + +Here is how the new definition of the bad sum fares in the three scenarios, +where the load is close to what would the relevant bounds be +if only full-length trials were used for the search. + +#### False Good Scenario + +If the duration is too short, we expect to see a higher frequency +of good short trials. +This could lead to a negative excess sum, +which has no impact, hence the load classification is given just by +full-length trials. +Thus, MLRsearch using too short trials has no detrimental effect +on result comparability in this scenario. +But also using short trials does not help with overall search duration, +probably making it worse. + +#### True Bad Scenario + +Settings with a small exceed ratio +have a small exceed coefficient, so the impact of the good short sum is small, +and the bad short sum is almost wholly converted into excess sum, +thus bad short trials have almost as big an impact as full-length bad trials. +The same conclusion applies to moderate exceed ratio values +when the good short sum is small. +Thus, short trials can cause a load to get classified as an upper bound earlier, +bringing time savings (while not affecting comparability). + +#### Balanced Scenario + +Here excess sum is small in absolute value, as the balancing sum +is expected to be similar to the bad short sum. +Once again, full-length trials are needed for final load classification; +but usage of short trials probably means MLRsearch needed +a shorter overall search time before selecting this load for measurement, +thus bringing time savings (while not affecting comparability). + +Note that in presence of short trial results, +the comparibility between the load classification +and the Conditional Throughput is only partial. +The Conditional Throughput still comes from a good long trial, +but a load higher than the Relevant Lower Bound may also compute to a good value. + +## Trials with Longer Duration + +If there are trial results with an intended duration larger +than the goal trial duration, the precise definitions +in Appendix A and Appendix B treat them in exactly the same way +as trials with duration equal to the goal trial duration. + +But in configurations with moderate (including 0.5) or small +Goal Exceed Ratio and small Goal Loss Ratio (especially zero), +bad trials with longer than goal durations may bias the search +towards the lower load values, as the noiseful end of the spectrum +gets a larger probability of causing the loss within the longer trials. + +{::comment} + [Use single goal when testing externaly, deviate freely in internal tests.] + + For some users, this is an acceptable price + for increased configuration flexibility + (perhaps saving time for the related goals), + so implementations SHOULD allow such configurations. + Still, users are encouraged to avoid such configurations + by making all goals use the same final trial duration, + so their results remain comparable across implementations. + + MKP2 mk edit note: This paragraph has no value in my view. + Statements like "For some users, this is an acceptable price + for increased configuration flexibility" do not make sense. + Configuration flexibility for flexibility sake is not a valid argument + in the specification that aims at standardising benchmarking methodologies. + If one wants to test with longer durations, + then one should configure these as Goal Final Trial Duration. + Simple, no? Or am I reading this point wrong? + +{:/comment} + +{::comment} + [MKP4 Out of scope here, subject for future work] + + # Current practices? + + MKP2 [VP] TODO: Even if not mentioned in spec (not even recommended), + some tricks from CSIT code may be worth mentioning? Not sure. + + MKP2 [VP] TODO: Tricks with big impact on search time + can be mentioned so that Addressed Problems : Long Test Duration + has something specific to refer to. + + MKP2 [VP] TODO: It is important to mention trick that have impact + on repeatability and comparability. + + MKP2 [VP] TODO: CSIT computes a discrete "grid" of load values to use. + + MKP2 [VP] TODO: + If all Goal Widths are aligned, there is one common coarse grid. + In that case, NDR (and even PDR conditional throughput + for tests with zer-or-big losses) values are identical in trending, + hiding the real performance variance, and causing fake anomaly + when the performance shifts just one gridpoint. + + + MKP2 [VP] TODO: Conversely, when Goal Width do not match well, + CSIT needs to compute a fine-grained grid to match them all. + In this case, similar performances can be "rounded differently", + mostly based on specific loss that happened at Max Load, + where SUT may be less stable than around PDR. + This way trending sees higher variance (still within corresponding Goal Width), + but at least there are no fake anomalies. + + + MKP2 [VP] TODO: In general, do not trust stdev if not larged than width. + + MKP2 [VP] TODO: De we have a chapter section fosucing on design principles? + - Make Controller API independent from Measurer API. + - The "allowed if makes worse" principle: + - RFC1242 specmanship happens when testing own DUTs. + - Shortening trial wait times only risks making goal results lower. + - So it is fine to save time aggressively when testing own DUTs. + + +{:/comment} + + +{::comment} + [Will be nice if made substantial.] + + # Addressed Problems + + MKP1 all of this section requires updating based on the updated content. + And it is for information only anyways. In fact not sure it's needed. + Maybe in appendix for posterity. + + Now when MLRsearch is clearly specified and explained, + it is possible to summarize how does MLRsearch specification help with problems. + + Here, "multiple trials" is a shorthand for having the goal final trial duration + significantly smaller than the Goal Duration Sum. + This results in MLRsearch performing multiple trials at the same load, + which may not be the case with other configurations. + + ## Long Test Duration + + As shortening the overall search duration is the main motivation + of MLRsearch library development, the library implements + multiple improvements on this front, both big and small. + + Most of implementation details are not constrained by MLRsearch specification, + so that future implementations may keep shortening the search duration even more. + + One exception is the impact of short trial results on the Relevant Lower Bound. + While motivated by human intuition, the logic is not straightforward. + In practice, configurations with only one common trial duration value + are capable of achieving good overal search time and result repeatability + without the need to consider short trials. + + ### Impact of goal attribute values + + From the required goal attributes, the Goal Duration Sum + remains the best way to get even shorter searches. + + Usage of multiple trials can also save time, + depending on wait times around trial traffic. + + The farther the Goal Exceed Ratio is from 0.5 (towards zero or one), + the less predictable the overal search duration becomes in practice. + + Width parameter does not change search duration much in practice + (compared to other, mainly optional goal attributes). + + ## DUT in SUT + + In practice, using multiple trials and moderate exceed ratios + often improves result repeatability without increasing the overall search time, + depending on the specific SUT and DUT characteristics. + Benefits for separating SUT noise are less clear though, + as it is not easy to distinguish SUT noise from DUT instability in general. + + Conditional Throughput has an intuitive meaning when described + using the performance spectrum, so this is an improvement + over existing simple (less configurable) search procedures. + + Multiple trials can save time also when the noisy end of + the preformance spectrum needs to be examined, e.g. for [RFC9004]. + + Under some circumstances, testing the same DUT and SUT setup with different + DUT configurations can give some hints on what part of noise is SUT noise + and what part is DUT performance fluctuations. + In practice, both types of noise tend to be too complicated for that analysis. + + MLRsearch enables users to search for multiple goals, + potentially providing more insight at the cost of a longer overall search time. + However, for a thorough and reliable examination of DUT-SUT interactions, + it is necessary to employ additional methods beyond black-box benchmarking, + such as collecting and analyzing DUT and SUT telemetry. + + ## Repeatability and Comparability + + Multiple trials improve repeatability, depending on exceed ratio. + + In practice, one-second goal final trial duration with exceed ratio 0.5 + is good enough for modern SUTs. + However, unless smaller wait times around the traffic part of the trial + are allowed, too much of overal search time would be wasted on waiting. + + It is not clear whether exceed ratios higher than 0.5 are better + for repeatability. + The 0.5 value is still preferred due to explainability using median. + + It is possible that the Conditional Throughput values (with non-zero goal + loss ratio) are better for repeatability than the Relevant Lower Bound values. + This is especially for implementations + which pick load from a small set of discrete values, + as that hides small variances in Relevant Lower Bound values + other implementations may find. + + Implementations focusing on shortening the overall search time + are automatically forced to avoid comparability issues due to load selection, + as they must prefer even splits wherever possible. + But this conclusion only holds when the same goals are used. + Larger adoption is needed before any further claims on comparability + between MLRsearch implementations can be made. + + ## Throughput with Non-Zero Loss + + Trivially suported by the Goal Loss Ratio attribute. + + In practice, usage of non-zero loss ratio values + improves the result repeatability + (exactly as expected based on results from simpler search methods). + + ## Inconsistent Trial Results + + MLRsearch is conservative wherever possible. + This is built into the definition of Conditional Throughput, + and into the treatment of short trial results for load classification. + + This is consistent with [RFC2544] zero loss tolerance motivation. + + If the noiseless part of the SUT performance spectrum is of interest, + it should be enough to set small value for the goal final trial duration, + and perhaps also a large value for the Goal Exceed Ratio. + + Implementations may offer other (optional) configuration attributes + to become less conservative, but currently it is not clear + what impact would that have on repeatability. + +{:/comment} + +# IANA Considerations + +No requests of IANA. + +# Security Considerations + +Benchmarking activities as described in this memo are limited to +technology characterization of a DUT/SUT using controlled stimuli in a +laboratory environment, with dedicated address space and the constraints +specified in the sections above. + +The benchmarking network topology will be an independent test setup and +MUST NOT be connected to devices that may forward the test traffic into +a production network or misroute traffic to the test management network. + +Further, benchmarking is performed on a "black-box" basis, relying +solely on measurements observable external to the DUT/SUT. + +Special capabilities SHOULD NOT exist in the DUT/SUT specifically for +benchmarking purposes. Any implications for network security arising +from the DUT/SUT SHOULD be identical in the lab and in production +networks. + +# Acknowledgements + +Some phrases and statements in this document were created +with help of Mistral AI (mistral.ai). + +Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough +review and numerous useful comments and suggestions in the earlier versions of this document. + +Special wholehearted gratitude and thanks to the late Al Morton for his +thorough reviews filled with very specific feedback and constructive +guidelines. Thank you Al for the close collaboration over the years, +for your continuous unwavering encouragement full of empathy and +positive attitude. Al, you are dearly missed. + +# Appendix A: Load Classification + +This section specifies how to perform the load classification. + +Any intended load value can be classified, according to a given [Search Goal] (#Search-Goal). + +The algorithm uses (some subsets of) the set of all available trial results +from trials measured at a given intended load at the end of the search. +All durations are those returned by the Measurer. + +The block at the end of this appendix holds pseudocode +which computes two values, stored in variables named +`optimistic` and `pessimistic`. + +{::comment} + [We have other section re optimistic. Not going to talk about variable naming here.] + + MKP2 mk edit note: Need to add the description of what + the `optimistic` and `pessimistic` variables represent. + Or a reference to where this is described + e.g. in [Single Trial Duration] (#Single-Trial-Duration) section. + +{:/comment} + +The pseudocode happens to be a valid Python code. + +If values of both variables are computed to be true, the load in question +is classified as a lower bound according to the given Search Goal. +If values of both variables are false, the load is classified as an upper bound. +Otherwise, the load is classified as undecided. + +The pseudocode expects the following variables to hold values as follows: + +- `goal_duration_sum`: The duration sum value of the given Search Goal. + +- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. + +- `good_long_sum`: Sum of durations across trials with trial duration + at least equal to the goal final trial duration and with a Trial Loss Ratio + not higher than the Goal Loss Ratio. + +- `bad_long_sum`: Sum of durations across trials with trial duration + at least equal to the goal final trial duration and with a Trial Loss Ratio + higher than the Goal Loss Ratio. + +- `good_short_sum`: Sum of durations across trials with trial duration + shorter than the goal final trial duration and with a Trial Loss Ratio + not higher than the Goal Loss Ratio. + +- `bad_short_sum`: Sum of durations across trials with trial duration + shorter than the goal final trial duration and with a Trial Loss Ratio + higher than the Goal Loss Ratio. + +The code works correctly also when there are no trial results at a given load. + +~~~ python +balancing_sum = good_short_sum * goal_exceed_ratio / (1.0 - goal_exceed_ratio) +effective_bad_sum = bad_long_sum + max(0.0, bad_short_sum - balancing_sum) +effective_whole_sum = max(good_long_sum + effective_bad_sum, goal_duration_sum) +quantile_duration_sum = effective_whole_sum * goal_exceed_ratio +optimistic = effective_bad_sum <= quantile_duration_sum +pessimistic = (effective_whole_sum - good_long_sum) <= quantile_duration_sum +~~~ + +# Appendix B: Conditional Throughput + +This section specifies how to compute Conditional Throughput, as referred to in section [Conditional Throughput] (#Conditional-Throughput). + +Any intended load value can be used as the basis for the following computation, +but only the Relevant Lower Bound (at the end of the search) +leads to the value called the Conditional Throughput for a given Search Goal. + +The algorithm uses (some subsets of) the set of all available trial results +from trials measured at a given intended load at the end of the search. +All durations are those returned by the Measurer. + +The block at the end of this appendix holds pseudocode +which computes a value stored as variable `conditional_throughput`. + +{::comment} + [CT is CT. But text could make more obvious.] + + MKP2 mk edit note: Need to add the description of what does + the `conditional_throughput` variable represent. + Or a reference to where this is described + e.g. in [Conditional Throughput] (#Conditional-Throughput) section. + +{:/comment} + +The pseudocode happens to be a valid Python code. + +The pseudocode expects the following variables to hold values as follows: + +- `goal_duration_sum`: The duration sum value of the given Search Goal. + +- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. + +- `good_long_sum`: Sum of durations across trials with trial duration + at least equal to the goal final trial duration and with a Trial Loss Ratio + not higher than the Goal Loss Ratio. + +- `bad_long_sum`: Sum of durations across trials with trial duration + at least equal to the goal final trial duration and with a Trial Loss Ratio + higher than the Goal Loss Ratio. + +- `long_trials`: An iterable of all trial results from trials with trial duration + at least equal to the goal final trial duration, + sorted by increasing the Trial Loss Ratio. + A trial result is a composite with the following two attributes available: + + - `trial.loss_ratio`: The Trial Loss Ratio as measured for this trial. + + - `trial.duration`: The trial duration of this trial. + +The code works correctly only when there if there is at least one +trial result measured at a given load. + +~~~ python +all_long_sum = max(goal_duration_sum, good_long_sum + bad_long_sum) +remaining = all_long_sum * (1.0 - goal_exceed_ratio) +quantile_loss_ratio = None +for trial in long_trials: + if quantile_loss_ratio is None or remaining > 0.0: + quantile_loss_ratio = trial.loss_ratio + remaining -= trial.duration + else: + break +else: + if remaining > 0.0: + quantile_loss_ratio = 1.0 +conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) +~~~ + +--- back + +{::comment} + [Final checklist.] + + [VP] Final Checks. Only mark as done when there are no active todos above. + + [VP] Rename chapter/sub-/section to better match their content. + + MKP3 [VP] TODO: Recheck the definition dependencies go bottom-up. + + [VP] TODO: Unify external reference style (brackets, spaces, section numbers and names). + + [VP] TODO: Add internal links wherever Captialized Term is mentioned. + + MKP2 [VP] TODO: Capitalization of New Terms: useful when editing and reviewing, + but I still vote to remove capitalization before final submit, + because all other RFCs I see only capitalize due to being section title. + + [VP] TODO: If time permits, keep improving formal style (e.g. using AI). + +{:/comment} -- cgit 1.2.3-korg