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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 @@ -<?xml version="1.0" encoding="us-ascii"?> - <?xml-stylesheet type="text/xsl" href="rfc2629.xslt" ?> - <!-- generated by https://github.com/cabo/kramdown-rfc version 1.7.18 (Ruby 3.1.2) --> - - -<!DOCTYPE rfc [ - <!ENTITY nbsp " "> - <!ENTITY zwsp "​"> - <!ENTITY nbhy "‑"> - <!ENTITY wj "⁠"> - -<!ENTITY RFC1242 SYSTEM "https://bib.ietf.org/public/rfc/bibxml/reference.RFC.1242.xml"> -<!ENTITY RFC2285 SYSTEM "https://bib.ietf.org/public/rfc/bibxml/reference.RFC.2285.xml"> -<!ENTITY RFC2544 SYSTEM "https://bib.ietf.org/public/rfc/bibxml/reference.RFC.2544.xml"> -<!ENTITY RFC8219 SYSTEM "https://bib.ietf.org/public/rfc/bibxml/reference.RFC.8219.xml"> -<!ENTITY RFC9004 SYSTEM "https://bib.ietf.org/public/rfc/bibxml/reference.RFC.9004.xml"> -]> - - -<rfc ipr="trust200902" docName="draft-ietf-bmwg-mlrsearch-07" category="info" tocInclude="true" sortRefs="true" symRefs="true"> - <front> - <title abbrev="MLRsearch">Multiple Loss Ratio Search</title> - - <author initials="M." surname="Konstantynowicz" fullname="Maciek Konstantynowicz"> - <organization>Cisco Systems</organization> - <address> - <email>mkonstan@cisco.com</email> - </address> - </author> - <author initials="V." surname="Polak" fullname="Vratko Polak"> - <organization>Cisco Systems</organization> - <address> - <email>vrpolak@cisco.com</email> - </address> - </author> - - <date year="2024" month="July" day="18"/> - - <area>ops</area> - <workgroup>Benchmarking Working Group</workgroup> - <keyword>Internet-Draft</keyword> - - <abstract> - - -<?line 52?> - -<t>This document proposes extensions to <xref target="RFC2544"></xref> 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.</t> - -<t>The primary reason for extending <xref target="RFC2544"></xref> is to address the challenges -and requirements presented by the evaluation and testing -of software-based networking systems' data planes.</t> - -<t>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.</t> - - - - </abstract> - - - - </front> - - <middle> - - -<?line 69?> - - -<section anchor="purpose-and-scope"><name>Purpose and Scope</name> - -<t>The purpose of this document is to describe Multiple Loss Ratio search -(MLRsearch), a data plane throughput search methodology optimized for software -networking DUTs.</t> - -<t>Applying vanilla <xref target="RFC2544"></xref> throughput bisection to software DUTs -results in several problems:</t> - -<t><list style="symbols"> - <t>Binary search takes too long as most trials are done far from the -eventually found throughput.</t> - <t>The required final trial duration and pauses between trials -prolong the overall search duration.</t> - <t>Software DUTs show noisy trial results, -leading to a big spread of possible discovered throughput values.</t> - <t>Throughput requires a loss of exactly zero frames, but the industry -frequently allows for small but non-zero losses.</t> - <t>The definition of throughput is not clear when trial results are inconsistent.</t> -</list></t> - -<t>To address the problems mentioned above, -the MLRsearch test methodology specification employs the following enhancements:</t> - -<t><list style="symbols"> - <t>Allow multiple short trials instead of one big trial per load. - <list style="symbols"> - <t>Optionally, tolerate a percentage of trial results with higher loss.</t> - </list></t> - <t>Allow searching for multiple Search Goals, with differing loss ratios. - <list style="symbols"> - <t>Any trial result can affect each Search Goal in principle.</t> - </list></t> - <t>Insert multiple coarse targets for each Search Goal, earlier ones need -to spend less time on trials. - <list style="symbols"> - <t>Earlier targets also aim for lesser precision.</t> - <t>Use Forwarding Rate (FR) at maximum offered load -<xref target="RFC2285"></xref> (section 3.6.2) to initialize the initial targets.</t> - </list></t> - <t>Take care when dealing with inconsistent trial results. - <list style="symbols"> - <t>Reported throughput is smaller than the smallest load with high loss.</t> - <t>Smaller load candidates are measured first.</t> - </list></t> - <t>Apply several load selection heuristics to save even more time -by trying hard to avoid unnecessarily narrow bounds.</t> -</list></t> - -<t>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.</t> - -<t>MLRsearch configuration options are flexible enough to -support both conservative settings and aggressive settings. -The conservative settings lead to results -unconditionally compliant with <xref target="RFC2544"></xref>, -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 <xref target="RFC2544"></xref>.</t> - -<t>No part of <xref target="RFC2544"></xref> is intended to be obsoleted by this document.</t> - -</section> -<section anchor="identified-problems"><name>Identified Problems</name> - -<t>This chapter describes the problems affecting usability -of various performance testing methodologies, -mainly a binary search for <xref target="RFC2544"></xref> unconditionally compliant throughput.</t> - -<section anchor="long-search-duration"><name>Long Search Duration</name> - - -<t>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.</t> - -<t>The current <xref target="RFC2544"></xref> 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.</t> - -<t>The bisection method, when unconditionally compliant with <xref target="RFC2544"></xref>, -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.</t> - -<t><xref target="RFC2544"></xref> 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.</t> - -</section> -<section anchor="dut-in-sut"><name>DUT in SUT</name> - -<t><xref target="RFC2285"></xref> defines: -- DUT as - - The network forwarding device to which stimulus is offered and - response measured <xref target="RFC2285"></xref> (section 3.1.1). -- SUT as - - The collective set of network devices to which stimulus is offered - as a single entity and response measured <xref target="RFC2285"></xref> (section 3.1.2).</t> - -<t><xref target="RFC2544"></xref> 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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>Unless specified otherwise, this document's focus is -on the potentially observable ends of the SUT performance spectrum, -as opposed to the extreme ones.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="repeatability-and-comparability"><name>Repeatability and Comparability</name> - -<t><xref target="RFC2544"></xref> 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.</t> - -<t><xref target="RFC2544"></xref> 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.</t> - -<t>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 <xref target="RFC2544"></xref>.</t> - -<t>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.</t> - -<t>Conversely, any alteration to <xref target="RFC2544"></xref> throughput search -that improves repeatability should be considered -as less dependent on the SUT noise.</t> - -<t>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.</t> - -</section> -<section anchor="throughput-with-non-zero-loss"><name>Throughput with Non-Zero Loss</name> - -<t><xref target="RFC1242"></xref> (section 3.17 Throughput) defines throughput as: - The maximum rate at which none of the offered frames - are dropped by the device.</t> - -<t>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.</t> - -<t>However, many benchmarking teams accept a small, -non-zero loss ratio as the goal for their load search.</t> - -<t>Motivations are many:</t> - -<t><list style="symbols"> - <t>Modern protocols tolerate frame loss better, -compared to the time when <xref target="RFC1242"></xref> and <xref target="RFC2544"></xref> were specified.</t> - <t>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.</t> - <t>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.</t> - <t>If an approximation of the SUT noise impact on the Trial Loss Ratio is known, -it can be set as the Goal Loss Ratio.</t> -</list></t> - -<t>Regardless of the validity of all similar motivations, -support for non-zero loss goals makes any search algorithm more user-friendly. -<xref target="RFC2544"></xref> throughput is not user-friendly in this regard.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="inconsistent-trial-results"><name>Inconsistent Trial Results</name> - -<t>While performing throughput search by executing a sequence of -measurement trials, there is a risk of encountering inconsistencies -between trial results.</t> - -<t>The plain bisection never encounters inconsistent trials. -But <xref target="RFC2544"></xref> 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.</t> - -<t>Examples include:</t> - -<t><list style="symbols"> - <t>A trial at the same load (same or different trial duration) results -in a different Trial Loss Ratio.</t> - <t>A trial at a higher load (same or different trial duration) results -in a smaller Trial Loss Ratio.</t> -</list></t> - -<t>Any robust throughput search algorithm needs to decide how to continue -the search in the presence of such inconsistencies. -Definitions of throughput in <xref target="RFC1242"></xref> and <xref target="RFC2544"></xref> are not specific enough -to imply a unique way of handling such inconsistencies.</t> - -<t>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.</t> - -</section> -</section> -<section anchor="mlrsearch-specification"><name>MLRsearch Specification</name> - -<t>This section describes MLRsearch specification including all technical -definitions needed for evaluating whether a particular test procedure -complies with MLRsearch specification.</t> - - -<section anchor="overview"><name>Overview</name> - -<t>MLRsearch specification describes a set of abstract system components, -acting as functions with specified inputs and outputs.</t> - -<t>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.</t> - -<t>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).</t> - - -<t>The Manager calls the Controller once, -the Controller keeps calling the Measurer -until all stopping conditions are met.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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).</t> - -</section> -<section anchor="measurement-quantities"><name>Measurement Quantities</name> - -<t>MLRsearch specification uses a number of measurement quantities.</t> - -<t>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.</t> - -<t>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.</t> - -<t>Some attributes are not independent from others, -and they can be calculated from other attributes. -Such quantites are called derived quantities.</t> - -</section> -<section anchor="existing-terms"><name>Existing Terms</name> - -<t>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.</t> - -<t>All three documents should be consulted -before attempting to make use of this document.</t> - -<t>Definitions of some central terms are copied and discussed in subsections.</t> - - - - - -<section anchor="sut"><name>SUT</name> - -<t>Defined in <xref target="RFC2285"></xref> (section 3.1.2 System Under Test (SUT)) as follows.</t> - -<t>Definition:</t> - -<t>The collective set of network devices to which stimulus is offered -as a single entity and response measured.</t> - -<t>Discussion:</t> - -<t>An SUT consisting of a single network device is also allowed.</t> - -</section> -<section anchor="dut"><name>DUT</name> - -<t>Defined in <xref target="RFC2285"></xref> (section 3.1.1 Device Under Test (DUT)) as follows.</t> - -<t>Definition:</t> - -<t>The network forwarding device to which stimulus is offered and -response measured.</t> - -<t>Discussion:</t> - -<t>DUT, as a sub-component of SUT, is only indirectly mentioned -in MLRsearch specification, but is of key relevance for its motivation.</t> - - -</section> -<section anchor="trial"><name>Trial</name> - -<t>A trial is the part of the test described in <xref target="RFC2544"></xref> (section 23. Trial description).</t> - -<t>Definition:</t> - -<t>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:</t> - -<t>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.</t> - -<t>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.</t> - -<t>c) Run the test trial.</t> - -<t>d) Wait for two seconds for any residual frames to be received.</t> - -<t>e) Wait for at least five seconds for the DUT to restabilize.</t> - -<t>Discussion:</t> - -<t>The definition describes some traits, it is not clear whether all of them -are REQUIRED, or some of them are only RECOMMENDED.</t> - - -<t>For the purposes of the MLRsearch specification, -it is ALLOWED for the test procedure to deviate from the <xref target="RFC2544"></xref> description, -but any such deviation MUST be made explicit in the test report.</t> - -<t>Trials are the only stimuli the SUT is expected to experience -during the search.</t> - -<t>In some discussion paragraphs, it is useful to consider the traffic -as sent and received by a tester, as implicitly defined -in <xref target="RFC2544"></xref> (section 6. Test set up).</t> - -<t>An example of deviation from <xref target="RFC2544"></xref> is using shorter wait times.</t> - -</section> -</section> -<section anchor="trial-terms"><name>Trial Terms</name> - -<t>This section defines new and redefine existing terms for quantities -relevant as inputs or outputs of trial, as used by the Measurer component.</t> - -<section anchor="trial-duration"><name>Trial Duration</name> - -<t>Definition:</t> - -<t>Trial duration is the intended duration of the traffic for a trial.</t> - -<t>Discussion:</t> - -<t>In general, this quantity does not include any preparation nor waiting -described in section 23 of <xref target="RFC2544"></xref> (section 23. Trial description).</t> - -<t>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.</t> - -</section> -<section anchor="trial-load"><name>Trial Load</name> - -<t>Definition:</t> - -<t>The trial load is the intended load for a trial</t> - -<t>Discussion:</t> - -<t>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 <xref target="RFC2544"></xref> (section 21. Bursty traffic), -but the test report MUST explicitly mention how non-constant the traffic is.</t> - -<t>Trial load is the quantity defined as Constant Load of <xref target="RFC1242"></xref> -(section 3.4 Constant Load), Data Rate of <xref target="RFC2544"></xref> -(section 14. Bidirectional traffic) -and Intended Load of <xref target="RFC2285"></xref> (section 3.5.1 Intended load (Iload)). -All three definitions specify -that this value applies to one (input or output) interface.</t> - - -<t>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.</t> - -<t>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.</t> - - -<t>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.</t> - - -</section> -<section anchor="trial-input"><name>Trial Input</name> - -<t>Definition:</t> - -<t>Trial Input is a composite quantity, consisting of two attributes: -trial duration and trial load.</t> - -<t>Discussion:</t> - -<t>When talking about multiple trials, it is common to say "Trial Inputs" -to denote all corresponding Trial Input instances.</t> - -<t>A Trial Input instance acts as the input for one call of the Measurer component.</t> - -<t>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.</t> - -</section> -<section anchor="traffic-profile"><name>Traffic Profile</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>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.</t> - -<t>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.</t> - -<t>The traffic profile SHOULD contain some specific quantities, -for example <xref target="RFC2544"></xref> (section 9. Frame sizes) governs -data link frame size as defined in <xref target="RFC1242"></xref> (section 3.5 Data link frame size).</t> - -<t>Several more specific quantities may be RECOMMENDED, depending on media type. -For example, <xref target="RFC2544"></xref> (Appendix C) lists frame formats and protocol addresses, -as recommended from <xref target="RFC2544"></xref> (section 8. Frame formats) -and <xref target="RFC2544"></xref> (section 12. Protocol addresses).</t> - -<t>Depending on SUT configuration, e.g. when testing specific protocols, -additional attributes MUST be included in the traffic profile -and in the test report.</t> - -<t>Example: <xref target="RFC8219"></xref> (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.</t> - -<t>Other traffic properties that need to be somehow specified -in Traffic Profile include: -<xref target="RFC2544"></xref> (section 14. Bidirectional traffic), -<xref target="RFC2285"></xref> (section 3.3.3 Fully meshed traffic), -and <xref target="RFC2544"></xref> (section 11. Modifiers).</t> - -</section> -<section anchor="trial-forwarding-ratio"><name>Trial Forwarding Ratio</name> - -<t>Definition:</t> - -<t>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</t> - -<t>Discussion:</t> - -<t>For most traffic profiles, "expected to be forwarded" means -"intended to get transmitted from Tester towards SUT".</t> - -<t>Trial forwarding ratio MAY be expressed in other units -(e.g. as a percentage) in the test report.</t> - -<t>Note that, contrary to loads, frame counts used to compute -trial forwarding ratio are aggregates over all SUT output interfaces.</t> - -<t>Questions around what is the correct number of frames -that should have been forwarded -is generally outside of the scope of this document.</t> - - - -</section> -<section anchor="trial-loss-ratio"><name>Trial Loss Ratio</name> - -<t>Definition:</t> - -<t>The Trial Loss Ratio is equal to one minus the trial forwarding ratio.</t> - -<t>Discussion:</t> - -<t>100% minus the trial forwarding ratio, when expressed as a percentage.</t> - -<t>This is almost identical to Frame Loss Rate of <xref target="RFC1242"></xref> -(section 3.6 Frame Loss Rate), -the only minor difference is that Trial Loss Ratio -does not need to be expressed as a percentage.</t> - -</section> -<section anchor="trial-forwarding-rate"><name>Trial Forwarding Rate</name> - -<t>Definition:</t> - -<t>The trial forwarding rate is a derived quantity, calculated by -multiplying the trial load by the trial forwarding ratio.</t> - -<t>Discussion:</t> - -<t>It is important to note that while similar, this quantity is not identical -to the Forwarding Rate as defined in <xref target="RFC2285"></xref> -(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.</t> - - -</section> -<section anchor="trial-effective-duration"><name>Trial Effective Duration</name> - -<t>Definition:</t> - -<t>Trial effective duration is a time quantity related to the trial, -by default equal to the trial duration.</t> - -<t>Discussion:</t> - -<t>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.</t> - -<t>Trial effective duration may be any time quantity chosen by the Measurer -to be used for time-based decisions in the Controller.</t> - -<t>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.</t> - -<t>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.</t> - -<t>Also, this is a way for the Measurer to inform the Controller about -its surprising behavior, for example when rounding the trial duration value.</t> - - -</section> -<section anchor="trial-output"><name>Trial Output</name> - -<t>Definition:</t> - -<t>Trial Output is a composite quantity. The REQUIRED attributes are -Trial Loss Ratio, trial effective duration and trial forwarding rate.</t> - -<t>Discussion:</t> - -<t>When talking about multiple trials, it is common to say "Trial Outputs" -to denote all corresponding Trial Output instances.</t> - -<t>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.</t> - -<t>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.</t> - -<t>While <xref target="RFC2285"></xref> (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.</t> - - -</section> -<section anchor="trial-result"><name>Trial Result</name> - -<t>Definition:</t> - -<t>Trial result is a composite quantity, -consisting of the Trial Input and the Trial Output.</t> - -<t>Discussion:</t> - -<t>When talking about multiple trials, it is common to say "trial results" -to denote all corresponding trial result instances.</t> - -<t>While implementations SHOULD NOT include additional attributes -with independent values, they MAY include derived quantities.</t> - -</section> -</section> -<section anchor="goal-terms"><name>Goal Terms</name> - -<t>This section defines new and redefine existing terms for quantities -indirectly relevant for inputs or outputs of the Controller component.</t> - -<t>Several goal attributes are defined before introducing -the main component quantity: the Search Goal.</t> - -<section anchor="goal-final-trial-duration"><name>Goal Final Trial Duration</name> - -<t>Definition:</t> - -<t>A threshold value for trial durations.</t> - -<t>Discussion:</t> - -<t>This attribute value MUST be positive.</t> - -<t>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).</t> - -<t>A trial that is not full-length is called a short trial.</t> - -<t>Informally, while MLRsearch is allowed to perform short trials, -the results from such short trials have only limited impact on search results.</t> - -<t>One trial may be full-length for some Search Goals, but not for others.</t> - -<t>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.</t> - -</section> -<section anchor="goal-duration-sum"><name>Goal Duration Sum</name> - -<t>Definition:</t> - -<t>A threshold value for a particular sum of trial effective durations.</t> - -<t>Discussion:</t> - -<t>This attribute value MUST be positive.</t> - -<t>Informally, even when looking only at full-length trials, -MLRsearch may spend up to this time measuring the same load value.</t> - -<t>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.</t> - -<t>See [TST009 Example] (#TST009-Example) for an example where possibility -of multiple full-length trials at the same load is intended.</t> - -<t>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.</t> - -</section> -<section anchor="goal-loss-ratio"><name>Goal Loss Ratio</name> - -<t>Definition:</t> - -<t>A threshold value for Trial Loss Ratios.</t> - -<t>Discussion:</t> - -<t>Attribute value MUST be non-negative and smaller than one.</t> - -<t>A trial with Trial Loss Ratio larger than a Goal Loss Ratio value -is called a lossy trial, with respect to given Search Goal.</t> - -<t>Informally, if a load causes too many lossy trials, -the Relevant Lower Bound for this goal will be smaller than that load.</t> - -<t>If a trial is not lossy, it is called a low-loss trial, -or (specifically for zero Goal Loss Ratio value) zero-loss trial.</t> - -</section> -<section anchor="goal-exceed-ratio"><name>Goal Exceed Ratio</name> - -<t>Definition:</t> - -<t>A threshold value for a particular ratio of sums of Trial Effective Durations.</t> - -<t>Discussion:</t> - -<t>Attribute value MUST be non-negative and smaller than one.</t> - -<t>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).</t> - -<t>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).</t> - -</section> -<section anchor="goal-width"><name>Goal Width</name> - -<t>Definition:</t> - -<t>A value used as a threshold for deciding -whether two trial load values are close enough.</t> - -<t>Discussion:</t> - -<t>If present, the value MUST be positive.</t> - -<t>Informally, this acts as a stopping condition, -controlling the precision of the search. -The search stops if every goal has reached its precision.</t> - -<t>Implementations without this attribute -MUST give the Controller other ways to control the search stopping conditions.</t> - -<t>Absolute load difference and relative load difference are two popular choices, -but implementations may choose a different way to specify width.</t> - -<t>The test report MUST make it clear what specific quantity is used as Goal Width.</t> - -<t>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.)</t> - -</section> -<section anchor="search-goal"><name>Search Goal</name> - -<t>Definition:</t> - -<t>The Search Goal is a composite quantity consisting of several attributes, -some of them are required.</t> - -<t>Required attributes: -- Goal Final Trial Duration -- Goal Duration Sum -- Goal Loss Ratio -- Goal Exceed Ratio</t> - -<t>Optional attribute: -- Goal Width</t> - -<t>Discussion:</t> - -<t>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.</t> - -<t>The meaning of listed attributes is formally given only by their indirect effect -on the search results.</t> - -<t>Informally, later sections provide additional intuitions and examples -of the Search Goal attribute values.</t> - -<t>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.</t> - -</section> -<section anchor="controller-input"><name>Controller Input</name> - -<t>Definition:</t> - -<t>Controller Input is a composite quantity -required as an input for the Controller. -The only REQUIRED attribute is a list of Search Goal instances.</t> - -<t>Discussion:</t> - -<t>MLRsearch implementations MAY use additional attributes. -Those additional attributes may be required by the implementation -even if they are not required by MLRsearch specification.</t> - -<t>Formally, the Manager does not apply any Controller configuration -apart from one Controller Input instance.</t> - -<t>For example, Traffic Profile is configured on the Measurer by the Manager -(without explicit assistance of the Controller).</t> - -<t>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.</t> - -<t>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 <xref target="RFC2544"></xref> (section 20. Maximum frame rate) -and <xref target="RFC2285"></xref> (3.5.3 Maximum offered load (MOL)), -some implementations may detect or discover it instead.</t> - - - -<t>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.</t> - - -</section> -</section> -<section anchor="search-goal-examples"><name>Search Goal Examples</name> - -<section anchor="rfc2544-goal"><name>RFC2544 Goal</name> - -<t>The following set of values makes the search result unconditionally compliant -with <xref target="RFC2544"></xref> (section 24 Trial duration)</t> - -<t><list style="symbols"> - <t>Goal Final Trial Duration = 60 seconds</t> - <t>Goal Duration Sum = 60 seconds</t> - <t>Goal Loss Ratio = 0%</t> - <t>Goal Exceed Ratio = 0%</t> -</list></t> - -<t>The latter two attributes are enough to make the search goal -conditionally compliant, adding the first attribute -makes it unconditionally compliant.</t> - -<t>The second attribute (Goal Duration Sum) only prevents MLRsearch -from repeating zero-loss full-length trials.</t> - -<t>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 <xref target="RFC2544"></xref> alone, it is not clear whether that higher load -could be considered as compliant throughput.</t> - -</section> -<section anchor="tst009-goal"><name>TST009 Goal</name> - -<t>One of the alternatives to RFC2544 is described in -<xref target="TST009"></xref> (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.</t> - -<t>Only the variant with "z = infinity" is achievable with MLRsearch.</t> - - -<t>For example, for "r = 2" variant, the following search goal should be used:</t> - -<t><list style="symbols"> - <t>Goal Final Trial Duration = 60 seconds</t> - <t>Goal Duration Sum = 120 seconds</t> - <t>Goal Loss Ratio = 0%</t> - <t>Goal Exceed Ratio = 50%</t> -</list></t> - -<t>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.</t> - -<t>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.</t> - -</section> -</section> -<section anchor="result-terms"><name>Result Terms</name> - -<t>Before defining the output of the Controller, -it is useful to define what the Goal Result is.</t> - -<t>The Goal Result is a composite quantity.</t> - -<t>Following subsections define its attribute first, before describing the Goal Result quantity.</t> - -<t>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.</t> - -<t>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.</t> - -<t>Before defining real attributes of the goal result, -it is useful to define bounds in general.</t> - -<section anchor="relevant-upper-bound"><name>Relevant Upper Bound</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>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.</t> - -<t>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.</t> - - -<t>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.</t> - - - -</section> -<section anchor="relevant-lower-bound"><name>Relevant Lower Bound</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>Only among loads smaller that the relevant upper bound, -the largest load becomes the relevant lower bound. -With loss inversion, stricter upper bound matters.</t> - -<t>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.</t> - -<t>Is no load had enough low-loss trials, the relevant lower bound -MAY not exist.</t> - - -<t>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.</t> - -<t>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.</t> - - -<t>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.</t> - - -</section> -<section anchor="conditional-throughput"><name>Conditional Throughput</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>Informally, this is a typical trial forwarding rate, expected to be seen -at the Relevant Lower Bound of the given Search Goal.</t> - -<t>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.</t> - -<t>This value is RECOMMENDED to be used when evaluating repeatability -and comparability if different MLRsearch implementations.</t> - - -</section> -<section anchor="goal-result"><name>Goal Result</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>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.</t> - - -<t>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.</t> - -<t>It is RECOMMENDED that the test report will display such results appropriately, -although MLRsearch specification does not prescibe how.</t> - - -<t>Anything else regarging Irregular Goal Results, -including their role in stopping conditions of the search -is outside the scope of this document.</t> - -</section> -<section anchor="search-result"><name>Search Result</name> - -<t>Definition:</t> - -<t>The Search Result is a single composite object -that maps each Search Goal instance to a corresponding Goal Result instance.</t> - -<t>Discussion:</t> - -<t>Alternatively, the Search Result can be implemented as an ordered list -of the Goal Result instances, matching the order of Search Goal instances.</t> - - -<t>The Search Result (as a mapping) -MUST map from all the Search Goal instances present in the Controller Input.</t> - - - -</section> -<section anchor="controller-output"><name>Controller Output</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>MLRsearch implementation MAY return additional data in the Controller Output.</t> - - -</section> -</section> -<section anchor="mlrsearch-architecture"><name>MLRsearch Architecture</name> - - -<t>MLRsearch architecture consists of three main system components: -the Manager, the Controller, and the Measurer.</t> - -<t>The architecture also implies the presence of other components, -such as the SUT and the Tester (as a sub-component of the Measurer).</t> - -<t>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.</t> - -<section anchor="measurer"><name>Measurer</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>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).</t> - -<t>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.</t> - -<t>Implementers have some freedom. -For example <xref target="RFC2544"></xref> (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.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="controller"><name>Controller</name> - -<t>Definition:</t> - -<t>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.</t> - -<t>Discussion:</t> - -<t>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.</t> - -<t>The Controller's role in optimizing the overall search time -distinguishes MLRsearch algorithms from simpler search procedures.</t> - -<t>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.</t> - -</section> -<section anchor="manager"><name>Manager</name> - -<t>Definition:</t> - -<t>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 <xref target="RFC2544"></xref> (section 26. Benchmarking tests).</t> - -<t>Discussion:</t> - -<t>The Manager initializes the SUT, the Measurer (and the Tester if independent) -with their intended configurations before calling the Controller.</t> - -<t>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.</t> - - -<t>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.</t> - - -</section> -</section> -<section anchor="implementation-compliance"><name>Implementation Compliance</name> - -<t>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.</t> - -<t>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.</t> - -<t>For example, any setup for conditionally (or unconditionally) -compliant <xref target="RFC2544"></xref> throughput testing -can be understood as a MLRsearch architecture, -assuming there is enough data to reconstruct the Relevant Upper Bound.</t> - -<t>See [RFC2544 Goal] (#RFC2544-Goal) subsection for equivalent Search Goal.</t> - -<t>Any test procedure that can be understood as (one call to the Manager of) -MLRsearch architecture is said to be compliant with MLRsearch specification.</t> - -</section> -</section> -<section anchor="additional-considerations"><name>Additional Considerations</name> - -<t>This section focuses on additional considerations, intuitions and motivations -pertaining to MLRsearch methodology.</t> - - -<section anchor="mlrsearch-versions"><name>MLRsearch Versions</name> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - - - -<t>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.</t> - - -<t>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.</t> - -</section> -<section anchor="stopping-conditions"><name>Stopping Conditions</name> - -<t><xref target="RFC2544"></xref> prescribes that after performing one trial at a specific offered load, -the next offered load should be larger or smaller, based on frame loss.</t> - -<t>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.</t> - -<t>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.</t> - -<t>After some number of trials, the tightest lower bound becomes the throughput. -<xref target="RFC2544"></xref> does not specify when, if ever, should the search stop.</t> - -<t>MLRsearch introduces a concept of [Goal Width] (#Goal-Width).</t> - -<t>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.</t> - -<t>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.</t> - -<t>This allows MLRsearch implementations to use stopping conditions -different from Goal Width.</t> - -</section> -<section anchor="load-classification"><name>Load Classification</name> - -<t>MLRsearch keeps the basic logic of binary search (tracking tightest bounds, -measuring at the middle), perhaps with minor technical differences.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>For repeatability and comparability reasons, it is important that -given a set of trial results, all implementations of MLRsearch -classify the load equivalently.</t> - -</section> -<section anchor="loss-ratios"><name>Loss Ratios</name> - -<t>Another difference between MLRsearch and <xref target="RFC2544"></xref> binary search is in the goals of the search. -<xref target="RFC2544"></xref> has a single goal, -based on classifying full-length trials as either lossless or lossy.</t> - -<t>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 <xref target="RFC2544"></xref> throughput goal then simply becomes a zero Goal Loss Ratio. -Different goals also may have different Goal Widths.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="loss-inversion"><name>Loss Inversion</name> - -<t>In <xref target="RFC2544"></xref> throughput search using bisection, any load with a lossy trial -becomes a hard upper bound, meaning every subsequent trial has a smaller -intended load.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="exceed-ratio"><name>Exceed Ratio</name> - -<t>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 <xref target="RFC2544"></xref> 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.</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="duration-sum"><name>Duration Sum</name> - -<t>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.</t> - -<t>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.</t> - -<t>Due to the fact that the duration sum has a big impact -on the overall search duration, and <xref target="RFC2544"></xref> prescribes -wait intervals around trial traffic, -the MLRsearch algorithm is allowed to sum durations that are different -from the actual trial traffic durations.</t> - -<t>In the MLRsearch specification, the different duration values are called -[Trial Effective Duration] (#Trial-Effective-Duration).</t> - -</section> -<section anchor="short-trials"><name>Short Trials</name> - -<t>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.</t> - -<t>Section 24 of <xref target="RFC2544"></xref> 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.</t> - -<t>Any MLRsearch implementation may include its own configuration options -which control when and how MLRsearch chooses to use short trial durations.</t> - -<t>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.</t> - -<t>The presence of short trial results complicates the load classification logic.</t> - -<t>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.</t> - - - -</section> -<section anchor="throughput"><name>Throughput</name> - - -<t>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.</t> - -<t>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 <xref target="RFC2285"></xref> 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).</t> - -<t>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).</t> - -<t>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.</t> - -<t>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).</t> - -<t>In the hard limit example, 100.5 Mfps load will still have -only 100.0 Mfps forwarding rate, nicely confirming the known limitation.</t> - -<t>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.</t> - - - - -<t>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.</t> - -<t>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.</t> - -<t>Overall, this Conditional Throughput does behave well for comparability purposes.</t> - -</section> -<section anchor="search-time"><name>Search Time</name> - -<t>MLRsearch was primarily developed to reduce the time -required to determine a throughput, either the <xref target="RFC2544"></xref> 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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - - -</section> -<section anchor="rfc2544-compliance"><name><xref target="RFC2544"></xref> Compliance</name> - -<t>Some Search Goal instances lead to results compliant with RFC2544. -See [RFC2544 Goal] (#RFC2544-Goal) for more details -regarding both conditional and unconditional compliance.</t> - -<t>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 <xref target="RFC2544"></xref> throughput.</t> - -</section> -</section> -<section anchor="logic-of-load-classification"><name>Logic of Load Classification</name> - -<section anchor="introductory-remarks"><name>Introductory Remarks</name> - -<t>This chapter continues with explanations, -but this time more precise definitions are needed -for readers to follow the explanations.</t> - -<t>Descriptions in this section are wordy and implementers should read -[MLRsearch Specification] (#MLRsearch-Specification) section -and Appendices for more concise definitions.</t> - -<t>The two areas of focus here are load classification -and the Conditional Throughput.</t> - -<t>To start with [Performance Spectrum] (#Performance-Spectrum) -subsection contains definitions needed to gain insight -into what Conditional Throughput means. -Remaining subsections discuss load classification.</t> - -<t>For load classification, it is useful to define <strong>good trials</strong> and <strong>bad trials</strong>:</t> - -<t><list style="symbols"> - <t><strong>Bad trial</strong>: 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).</t> - <t><strong>Good trial</strong>: Trial that is not bad is called good.</t> -</list></t> - -</section> -<section anchor="performance-spectrum"><name>Performance Spectrum</name> -<t>### Description</t> - -<t>There are several equivalent ways to explain the Conditional Throughput -computation. One of the ways relies on performance -spectrum.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>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.</t> - -<t>These functions are related to the SUT performance spectrum, -as sampled by the trials in the set.</t> - - -<t>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.</t> - -<t>If the quantile touches two trials,</t> - - -<t>the larger trial forwarding rate (from the trial result sorted earlier) is used.</t> - - -<t>The resulting quantity is the Conditional Throughput of the goal in question.</t> - - -<t>A set of examples follows.</t> - -<section anchor="first-example"><name>First Example</name> - -<t><list style="symbols"> - <t>[Goal Exceed Ratio] (#Goal-Exceed-Ratio) = 0 and [Goal Duration Sum] (#Goal-Duration-Sum) has been reached.</t> - <t>Conditional Throughput is the smallest trial forwarding rate among the trials.</t> -</list></t> - -</section> -<section anchor="second-example"><name>Second Example</name> - -<t><list style="symbols"> - <t>Goal Exceed Ratio = 0 and Goal Duration Sum has not been reached yet.</t> - <t>Due to the missing duration sum, the worst case may still happen, so the Conditional Throughput is zero.</t> - <t>This is not reported to the user, as this load cannot become the Relevant Lower Bound yet.</t> -</list></t> - -</section> -<section anchor="third-example"><name>Third Example</name> - -<t><list style="symbols"> - <t>Goal Exceed Ratio = 50% and Goal Duration Sum is two seconds.</t> - <t>One trial is present with the duration of one second and zero loss.</t> - <t>The imaginary trial is added with the duration of one second and zero trial forwarding rate.</t> - <t>The median would touch both trials, so the Conditional Throughput is the trial forwarding rate of the one non-imaginary trial.</t> - <t>As that had zero loss, the value is equal to the offered load.</t> -</list></t> - - -</section> -<section anchor="summary"><name>Summary</name> - -<t>While the Conditional Throughput is a generalization of the trial forwarding rate, -its definition is not an obvious one.</t> - -<t>Other than the trial forwarding rate, the other source of intuition -is the quantile in general, and the median the recommended case.</t> - - -</section> -</section> -<section anchor="trials-with-single-duration"><name>Trials with Single Duration</name> - - -<t>When goal attributes are chosen in such a way that every trial has the same -intended duration, the load classification is simpler.</t> - -<t>The following description follows the motivation -of Goal Loss Ratio, Goal Exceed Ratio, and Goal Duration Sum.</t> - -<t>If the sum of the durations of all trials (at the given load) -is less than the Goal Duration Sum, imagine two scenarios:</t> - -<t><list style="symbols"> - <t><strong>best case scenario</strong>: all subsequent trials having zero loss, and</t> - <t><strong>worst case scenario</strong>: all subsequent trials having 100% loss.</t> -</list></t> - -<t>Here we assume there are as many subsequent trials as needed -to make the sum of all trials equal to the Goal Duration Sum.</t> - -<t>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.</t> - -<t>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.</t> - -<t>Even if, in the best case scenario, the load exceed ratio is larger -than the Goal Exceed Ratio, the load is an upper bound.</t> - -<t>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.</t> - - -<t>More specifically:</t> - -<t><list style="symbols"> - <t>Take all trials measured at a given load.</t> - <t>The sum of the durations of all bad full-length trials is called the bad sum.</t> - <t>The sum of the durations of all good full-length trials is called the good sum.</t> - <t>The result of adding the bad sum plus the good sum is called the measured sum.</t> - <t>The larger of the measured sum and the Goal Duration Sum is called the whole sum.</t> - <t>The whole sum minus the measured sum is called the missing sum.</t> - <t>The optimistic exceed ratio is the bad sum divided by the whole sum.</t> - <t>The pessimistic exceed ratio is the bad sum plus the missing sum, that divided by the whole sum.</t> - <t>If the optimistic exceed ratio is larger than the Goal Exceed Ratio, the load is classified as an upper bound.</t> - <t>If the pessimistic exceed ratio is not larger than the Goal Exceed Ratio, the load is classified as a lower bound.</t> - <t>Else, the load is classified as undecided.</t> -</list></t> - -<t>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.</t> - - -<t>If it is larger, the load is either an upper bound or undecided.</t> - -</section> -<section anchor="trials-with-short-duration"><name>Trials with Short Duration</name> - -<section anchor="scenarios"><name>Scenarios</name> - -<t>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?</t> - -<t>There are three main scenarios where human intuition guides -the intended behavior of load classification.</t> - -<section anchor="false-good-scenario"><name>False Good Scenario</name> - -<t>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.</t> - -<t>In any case, many good short trials may become bad full-length trials -in the counter-factual case.</t> - -<t>In extreme cases, there are plenty of good short trials and no bad short trials.</t> - -<t>In this scenario, we want the load classification NOT to classify the load -as a lower bound, despite the abundance of good short trials.</t> - - -<t>Effectively, we want the good short trials to be ignored, so they -do not contribute to comparisons with the Goal Duration Sum.</t> - -</section> -<section anchor="true-bad-scenario"><name>True Bad Scenario</name> - -<t>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.</t> - -<t>In practice, bad short trials are rarely turning into -good full-length trials.</t> - -<t>In extreme cases, there are no good short trials.</t> - -<t>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.</t> - -<t>Effectively, we want the bad short trials -to contribute to comparisons with the Goal Duration Sum, -so the load can be classified sooner.</t> - -</section> -<section anchor="balanced-scenario"><name>Balanced Scenario</name> - -<t>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).</t> - - -<t>For a moderate Goal Exceed Ratio value, this may mean there are both -good short trials and bad short trials.</t> - -<t>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.</t> - -<t>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.</t> - -<t>The only way to decide safely is to do more trials at full length, -the same as in False Good Scenario.</t> - -</section> -</section> -<section anchor="classification-logic"><name>Classification Logic</name> - -<t>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.</t> - -<t>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.</t> - -<t>Few more notions are needed for defining the new bad sum:</t> - -<t><list style="symbols"> - <t>The sum of durations of all bad full-length trials is called the bad long sum.</t> - <t>The sum of durations of all bad short trials is called the bad short sum.</t> - <t>The sum of durations of all good short trials is called the good short sum.</t> - <t>One minus the Goal Exceed Ratio is called the subceed ratio.</t> - <t>The Goal Exceed Ratio divided by the subceed ratio is called the exceed coefficient.</t> - <t>The good short sum multiplied by the exceed coefficient is called the balancing sum.</t> - <t>The bad short sum minus the balancing sum is called the excess sum.</t> - <t>If the excess sum is negative, the bad sum is equal to the bad long sum.</t> - <t>Otherwise, the bad sum is equal to the bad long sum plus the excess sum.</t> -</list></t> - -<t>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.</t> - -<section anchor="false-good-scenario-1"><name>False Good Scenario</name> - -<t>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.</t> - -</section> -<section anchor="true-bad-scenario-1"><name>True Bad Scenario</name> - -<t>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).</t> - -</section> -<section anchor="balanced-scenario-1"><name>Balanced Scenario</name> - -<t>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).</t> - -<t>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.</t> - -</section> -</section> -</section> -<section anchor="trials-with-longer-duration"><name>Trials with Longer Duration</name> - -<t>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.</t> - -<t>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.</t> - - - - -</section> -</section> -<section anchor="iana-considerations"><name>IANA Considerations</name> - -<t>No requests of IANA.</t> - -</section> -<section anchor="security-considerations"><name>Security Considerations</name> - -<t>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.</t> - -<t>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.</t> - -<t>Further, benchmarking is performed on a "black-box" basis, relying -solely on measurements observable external to the DUT/SUT.</t> - -<t>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.</t> - -</section> -<section anchor="acknowledgements"><name>Acknowledgements</name> - -<t>Some phrases and statements in this document were created -with help of Mistral AI (mistral.ai).</t> - -<t>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.</t> - -<t>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.</t> - -</section> -<section anchor="appendix-a-load-classification"><name>Appendix A: Load Classification</name> - -<t>This section specifies how to perform the load classification.</t> - -<t>Any intended load value can be classified, according to a given [Search Goal] (#Search-Goal).</t> - -<t>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.</t> - -<t>The block at the end of this appendix holds pseudocode -which computes two values, stored in variables named -<spanx style="verb">optimistic</spanx> and <spanx style="verb">pessimistic</spanx>.</t> - - -<t>The pseudocode happens to be a valid Python code.</t> - -<t>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.</t> - -<t>The pseudocode expects the following variables to hold values as follows:</t> - -<t><list style="symbols"> - <t><spanx style="verb">goal_duration_sum</spanx>: The duration sum value of the given Search Goal.</t> - <t><spanx style="verb">goal_exceed_ratio</spanx>: The exceed ratio value of the given Search Goal.</t> - <t><spanx style="verb">good_long_sum</spanx>: 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.</t> - <t><spanx style="verb">bad_long_sum</spanx>: 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.</t> - <t><spanx style="verb">good_short_sum</spanx>: 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.</t> - <t><spanx style="verb">bad_short_sum</spanx>: 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.</t> -</list></t> - -<t>The code works correctly also when there are no trial results at a given load.</t> - -<figure><sourcecode type="python"><![CDATA[ -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 -]]></sourcecode></figure> - -</section> -<section anchor="appendix-b-conditional-throughput"><name>Appendix B: Conditional Throughput</name> - -<t>This section specifies how to compute Conditional Throughput, as referred to in section [Conditional Throughput] (#Conditional-Throughput).</t> - -<t>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.</t> - -<t>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.</t> - -<t>The block at the end of this appendix holds pseudocode -which computes a value stored as variable <spanx style="verb">conditional_throughput</spanx>.</t> - - -<t>The pseudocode happens to be a valid Python code.</t> - -<t>The pseudocode expects the following variables to hold values as follows:</t> - -<t><list style="symbols"> - <t><spanx style="verb">goal_duration_sum</spanx>: The duration sum value of the given Search Goal.</t> - <t><spanx style="verb">goal_exceed_ratio</spanx>: The exceed ratio value of the given Search Goal.</t> - <t><spanx style="verb">good_long_sum</spanx>: 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.</t> - <t><spanx style="verb">bad_long_sum</spanx>: 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.</t> - <t><spanx style="verb">long_trials</spanx>: 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: <list style="symbols"> - <t><spanx style="verb">trial.loss_ratio</spanx>: The Trial Loss Ratio as measured for this trial.</t> - <t><spanx style="verb">trial.duration</spanx>: The trial duration of this trial.</t> - </list></t> -</list></t> - -<t>The code works correctly only when there if there is at least one -trial result measured at a given load.</t> - -<figure><sourcecode type="python"><![CDATA[ -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) -]]></sourcecode></figure> - -</section> - - - </middle> - - <back> - - -<references title='References' anchor="sec-combined-references"> - - <references title='Normative References' anchor="sec-normative-references"> - -&RFC1242; -&RFC2285; -&RFC2544; -&RFC8219; -&RFC9004; - - - </references> - - <references title='Informative References' anchor="sec-informative-references"> - -<reference anchor="TST009" target="https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.04.01_60/gs_NFV-TST009v030401p.pdf"> - <front> - <title>TST 009</title> - <author > - <organization></organization> - </author> - <date year="n.d."/> - </front> -</reference> -<reference anchor="FDio-CSIT-MLRsearch" target="https://csit.fd.io/cdocs/methodology/measurements/data_plane_throughput/mlr_search/"> - <front> - <title>FD.io CSIT Test Methodology - MLRsearch</title> - <author > - <organization></organization> - </author> - <date year="2023" month="October"/> - </front> -</reference> -<reference anchor="PyPI-MLRsearch" target="https://pypi.org/project/MLRsearch/1.2.1/"> - <front> - <title>MLRsearch 1.2.1, Python Package Index</title> - <author > - <organization></organization> - </author> - <date year="2023" month="October"/> - </front> -</reference> - - - </references> - -</references> 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