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diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-06.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-06.md deleted file mode 100644 index 27d65e2690..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-06.md +++ /dev/null @@ -1,1634 +0,0 @@ ---- - -title: Multiple Loss Ratio Search -abbrev: MLRsearch -docname: draft-ietf-bmwg-mlrsearch-06 -date: 2024-03-04 - -ipr: trust200902 -area: ops -wg: Benchmarking Working Group -kw: Internet-Draft -cat: info - -coding: us-ascii -pi: # can use array (if all yes) or hash here - toc: yes - sortrefs: # defaults to yes - symrefs: yes - -author: - - - ins: M. Konstantynowicz - name: Maciek Konstantynowicz - org: Cisco Systems - email: mkonstan@cisco.com - - - ins: V. Polak - name: Vratko Polak - org: Cisco Systems - email: vrpolak@cisco.com - -normative: - RFC1242: - RFC2285: - RFC2544: - RFC9004: - -informative: - TST009: - target: https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.04.01_60/gs_NFV-TST009v030401p.pdf - title: "TST 009" - FDio-CSIT-MLRsearch: - target: https://csit.fd.io/cdocs/methodology/measurements/data_plane_throughput/mlr_search/ - title: "FD.io CSIT Test Methodology - MLRsearch" - date: 2023-10 - PyPI-MLRsearch: - target: https://pypi.org/project/MLRsearch/1.2.1/ - title: "MLRsearch 1.2.1, Python Package Index" - date: 2023-10 - ---- abstract - -This document proposes extensions to [RFC2544] throughput search by -defining a new methodology called Multiple Loss Ratio search -(MLRsearch). MLRsearch aims to minimize search duration, -support multiple loss ratio searches, -and enhance result repeatability and comparability. - -The primary reason for extending [RFC2544] is to address the challenges -and requirements presented by the evaluation and testing -of software-based networking systems' data planes. - -To give users more freedom, MLRsearch provides additional configuration options -such as allowing multiple shorter trials per load instead of one large trial, -tolerating a certain percentage of trial results with higher loss, -and supporting the search for multiple goals with varying loss ratios. - ---- middle - -{::comment} - As we use Kramdown to convert from Markdown, - we use this way of marking comments not to be visible in the rendered draft. - https://stackoverflow.com/a/42323390 - If another engine is used, convert to this way: - https://stackoverflow.com/a/20885980 -{:/comment} - -# Purpose and Scope - -The purpose of this document is to describe Multiple Loss Ratio search -(MLRsearch), a data plane throughput search methodology optimized for software -networking DUTs. - -Applying vanilla [RFC2544] throughput bisection to software DUTs -results in several problems: - -- Binary search takes too long as most trials are done far from the - eventually found throughput. -- The required final trial duration and pauses between trials - prolong the overall search duration. -- Software DUTs show noisy trial results, - leading to a big spread of possible discovered throughput values. -- Throughput requires a loss of exactly zero frames, but the industry - frequently allows for small but non-zero losses. -- The definition of throughput is not clear when trial results are inconsistent. - - -To address the problems mentioned above, -the MLRsearch library employs the following enhancements: - -- Allow multiple shorter trials instead of one big trial per load. - - Optionally, tolerate a percentage of trial results with higher loss. -- Allow searching for multiple search goals, with differing loss ratios. - - Any trial result can affect each search goal in principle. -- Insert multiple coarse targets for each search goal, earlier ones need - to spend less time on trials. - - Earlier targets also aim for lesser precision. - - Use Forwarding Rate (FR) at maximum offered load - [RFC2285] (section 3.6.2) to initialize the initial targets. -- Take care when dealing with inconsistent trial results. - - Reported throughput is smaller than the smallest load with high loss. - - Smaller load candidates are measured first. -- Apply several load selection heuristics to save even more time - by trying hard to avoid unnecessarily narrow bounds. - -Some of these enhancements are formalized as MLRsearch specification, -the remaining enhancements are treated as implementation details, -thus achieving high comparability without limiting future improvements. - -MLRsearch configuration options are flexible enough to -support both conservative settings and aggressive settings. -Where the conservative settings lead to results -unconditionally compliant with [RFC2544], -but longer search duration and worse repeatability. -Conversely, aggressive settings lead to shorter search duration -and better repeatability, but the results are not compliant with [RFC2544]. - -No part of [RFC2544] is intended to be obsoleted by this document. - -# Identified Problems - -This chapter describes the problems affecting usability -of various performance testing methodologies, -mainly a binary search for [RFC2544] unconditionally compliant throughput. - -## Long Search Duration - -The emergence of software DUTs, with frequent software updates and a -number of different frame processing modes and configurations, -has increased both the number of performance tests -required to verify the DUT update and the frequency of running those tests. -This makes the overall test execution time even more important than before. - -The current [RFC2544] throughput definition restricts the potential -for time-efficiency improvements. -A more generalized throughput concept could enable further enhancements -while maintaining the precision of simpler methods. - -The bisection method, when unconditionally compliant with [RFC2544], -is excessively slow. -This is because a significant amount of time is spent on trials -with loads that, in retrospect, are far from the final determined throughput. - -[RFC2544] does not specify any stopping condition for throughput search, -so users already have an access to a limited trade-off -between search duration and achieved precision. -However, each full 60-second trials doubles the precision, -so not many trials can be removed without a substantial loss of precision. - -## DUT in SUT - -[RFC2285] defines: -- DUT as - - The network forwarding device to which stimulus is offered and - response measured [RFC2285] (section 3.1.1). -- SUT as - - The collective set of network devices to which stimulus is offered - as a single entity and response measured [RFC2285] (section 3.1.2). - -[RFC2544] specifies a test setup with an external tester stimulating the -networking system, treating it either as a single DUT, or as a system -of devices, an SUT. - -In the case of software networking, the SUT consists of not only the DUT -as a software program processing frames, but also of -a server hardware and operating system functions, -with server hardware resources shared across all programs -and the operating system running on the same server. - -Given that the SUT is a shared multi-tenant environment -encompassing the DUT and other components, the DUT might inadvertently -experience interference from the operating system -or other software operating on the same server. - -Some of this interference can be mitigated. -For instance, -pinning DUT program threads to specific CPU cores -and isolating those cores can prevent context switching. - -Despite taking all feasible precautions, some adverse effects may still impact -the DUT's network performance. -In this document, these effects are collectively -referred to as SUT noise, even if the effects are not as unpredictable -as what other engineering disciplines call noise. - -DUT can also exhibit fluctuating performance itself, for reasons -not related to the rest of SUT; for example due to pauses in execution -as needed for internal stateful processing. -In many cases this -may be an expected per-design behavior, as it would be observable even -in a hypothetical scenario where all sources of SUT noise are eliminated. -Such behavior affects trial results in a way similar to SUT noise. -As the two phenomenons are hard to distinguish, -in this document the term 'noise' is used to encompass -both the internal performance fluctuations of the DUT -and the genuine noise of the SUT. - -A simple model of SUT performance consists of an idealized noiseless performance, -and additional noise effects. -For a specific SUT, the noiseless performance is assumed to be constant, -with all observed performance variations being attributed to noise. -The impact of the noise can vary in time, sometimes wildly, -even within a single trial. -The noise can sometimes be negligible, but frequently -it lowers the observed SUT performance as observed in trial results. - -In this model, SUT does not have a single performance value, it has a spectrum. -One end of the spectrum is the idealized noiseless performance value, -the other end can be called a noiseful performance. -In practice, trial result -close to the noiseful end of the spectrum happens only rarely. -The worse the performance value is, the more rarely it is seen in a trial. -Therefore, the extreme noiseful end of the SUT spectrum is not observable -among trial results. -Also, the extreme noiseless end of the SUT spectrum -is unlikely to be observable, this time because some small noise effects -are likely to occur multiple times during a trial. - -Unless specified otherwise, this document's focus is -on the potentially observable ends of the SUT performance spectrum, -as opposed to the extreme ones. - -When focusing on the DUT, the benchmarking effort should ideally aim -to eliminate only the SUT noise from SUT measurements. -However, -this is currently not feasible in practice, as there are no realistic enough -models available to distinguish SUT noise from DUT fluctuations, -based on the author's experience and available literature. - -Assuming a well-constructed SUT, the DUT is likely its -primary performance bottleneck. -In this case, we can define the DUT's -ideal noiseless performance as the noiseless end of the SUT performance spectrum, -especially for throughput. -However, other performance metrics, such as latency, -may require additional considerations. - -Note that by this definition, DUT noiseless performance -also minimizes the impact of DUT fluctuations, as much as realistically possible -for a given trial duration. - -This document aims to solve the DUT in SUT problem -by estimating the noiseless end of the SUT performance spectrum -using a limited number of trial results. - -Any improvements to the throughput search algorithm, aimed at better -dealing with software networking SUT and DUT setup, should employ -strategies recognizing the presence of SUT noise, allowing the discovery of -(proxies for) DUT noiseless performance -at different levels of sensitivity to SUT noise. - -## Repeatability and Comparability - -[RFC2544] does not suggest to repeat throughput search. -And from just one -discovered throughput value, it cannot be determined how repeatable that value is. -Poor repeatability then leads to poor comparability, -as different benchmarking teams may obtain varying throughput values -for the same SUT, exceeding the expected differences from search precision. - -[RFC2544] throughput requirements (60 seconds trial and -no tolerance of a single frame loss) affect the throughput results -in the following way. -The SUT behavior close to the noiseful end of its performance spectrum -consists of rare occasions of significantly low performance, -but the long trial duration makes those occasions not so rare on the trial level. -Therefore, the binary search results tend to wander away from the noiseless end -of SUT performance spectrum, more frequently and more widely than shorter -trials would, thus causing poor throughput repeatability. - -The repeatability problem can be addressed by defining a search procedure -that identifies a consistent level of performance, -even if it does not meet the strict definition of throughput in [RFC2544]. - -According to the SUT performance spectrum model, better repeatability -will be at the noiseless end of the spectrum. -Therefore, solutions to the DUT in SUT problem -will help also with the repeatability problem. - -Conversely, any alteration to [RFC2544] throughput search -that improves repeatability should be considered -as less dependent on the SUT noise. - -An alternative option is to simply run a search multiple times, and report some -statistics (e.g. average and standard deviation). -This can be used -for a subset of tests deemed more important, -but it makes the search duration problem even more pronounced. - -## Throughput with Non-Zero Loss - -[RFC1242] (section 3.17) defines throughput as: - The maximum rate at which none of the offered frames - are dropped by the device. - -Then, it says: - Since even the loss of one frame in a - data stream can cause significant delays while - waiting for the higher level protocols to time out, - it is useful to know the actual maximum data - rate that the device can support. - -However, many benchmarking teams accept a small, -non-zero loss ratio as the goal for their load search. - -Motivations are many: - -- Modern protocols tolerate frame loss better, - compared to the time when [RFC1242] and [RFC2544] were specified. - -- Trials nowadays send way more frames within the same duration, - increasing the chance of a small SUT performance fluctuation - being enough to cause frame loss. - -- Small bursts of frame loss caused by noise have otherwise smaller impact - on the average frame loss ratio observed in the trial, - as during other parts of the same trial the SUT may work more closely - to its noiseless performance, thus perhaps lowering the trial loss ratio - below the goal loss ratio value. - -- If an approximation of the SUT noise impact on the trial loss ratio is known, - it can be set as the goal loss ratio. - -Regardless of the validity of all similar motivations, -support for non-zero loss goals makes any search algorithm more user-friendly. -[RFC2544] throughput is not user-friendly in this regard. - -Furthermore, allowing users to specify multiple loss ratio values, -and enabling a single search to find all relevant bounds, -significantly enhances the usefulness of the search algorithm. - -Searching for multiple search goals also helps to describe the SUT performance -spectrum better than the result of a single search goal. -For example, the repeated wide gap between zero and non-zero loss loads -indicates the noise has a large impact on the observed performance, -which is not evident from a single goal load search procedure result. - -It is easy to modify the vanilla bisection to find a lower bound -for the intended load that satisfies a non-zero goal loss ratio. -But it is not that obvious how to search for multiple goals at once, -hence the support for multiple search goals remains a problem. - -## Inconsistent Trial Results - -While performing throughput search by executing a sequence of -measurement trials, there is a risk of encountering inconsistencies -between trial results. - -The plain bisection never encounters inconsistent trials. -But [RFC2544] hints about the possibility of inconsistent trial results, -in two places in its text. -The first place is section 24, where full trial durations are required, -presumably because they can be inconsistent with the results -from shorter trial durations. -The second place is section 26.3, where two successive zero-loss trials -are recommended, presumably because after one zero-loss trial -there can be a subsequent inconsistent non-zero-loss trial. - -Examples include: - -- A trial at the same load (same or different trial duration) results - in a different trial loss ratio. -- A trial at a higher load (same or different trial duration) results - in a smaller trial loss ratio. - -Any robust throughput search algorithm needs to decide how to continue -the search in the presence of such inconsistencies. -Definitions of throughput in [RFC1242] and [RFC2544] are not specific enough -to imply a unique way of handling such inconsistencies. - -Ideally, there will be a definition of a new quantity which both generalizes -throughput for non-zero-loss (and other possible repeatability enhancements), -while being precise enough to force a specific way to resolve trial result -inconsistencies. -But until such a definition is agreed upon, the correct way to handle -inconsistent trial results remains an open problem. - -# MLRsearch Specification - -This chapter focuses on technical definitions needed for evaluating -whether a particular test procedure adheres to MLRsearch specification. - -For motivations, explanations, and other comments see other chapters. - -## MLRsearch Architecture - -MLRsearch architecture consists of three main components: -the manager, the controller, and the measurer. -For definitions of the components, see the following sections. - -The architecture also implies the presence of other components, such as the SUT. - -These components can be seen as abstractions present in any testing procedure. - -### Measurer - -The measurer is the component that performs one trial -as described in [RFC2544] section 23. - -Specifically, one call to the measurer accepts a trial load value -and trial duration value, performs the trial, and returns -the measured trial loss ratio, and optionally a different duration value. - -It is the responsibility of the measurer to uphold any requirements -and assumptions present in MLRsearch specification -(e.g. trial forwarding ratio not being larger than one). -Implementers have some freedom, for example in the way they deal with -duplicated frames, or what to return if the tester sent zero frames towards SUT. -Implementations are RECOMMENDED to document their behavior -related to such freedoms in as detailed a way as possible. - -Implementations MUST document any deviations from RFC documents, -for example if the wait time around traffic -is shorter than what [RFC2544] section 23 specifies. - -### Controller - -The controller selects trial load and duration values -to achieve the search goals in the shortest expected time. - -The controller calls the measurer multiple times, -receiving the trial result from each call. -After exit condition is met, the controller returns -the overall search results. - -The controller's role in optimizing trial load and duration selection -distinguishes MLRsearch algorithms from simpler search procedures. - -For controller inputs, see later section Controller Inputs. -For controller outputs, see later section Controller Outputs. - -### Manager - -The controller gets initiated by the manager once, and subsequently calls - -The manager is the component that initializes SUT, the traffic generator -(tester in [RFC2544] terminology), the measurer and the controller -with intended configurations. -It then calls the controller once, and receives its outputs. - -The manager is also responsible for creating reports in the appropriate format, -based on information in controller outputs. - -## Units - -The specification deals with physical quantities, so it is assumed -each numeric value is accompanied by an appropriate physical unit. - -The specification does not state which unit is appropriate, -but implementations MUST make it explicit which unit is used -for each value provided or received by the user. - -For example, load quantities (including the conditional throughput) -returned by the controller are defined to be based on a single-interface -(unidirectional) loads. -For bidirectional traffic, users are likely -to expect bidirectional throughput quantities, so the manager is responsible -for making its report clear. - -## SUT - -As defined in [RFC2285]: -The collective set of network devices to which stimulus is offered -as a single entity and response measured. - -## Trial - -A trial is the part of the test described in [RFC2544] section 23. - -### Trial Load - -The trial load is the intended constant load for a trial. - -Load is the quantity implied by Constant Load of [RFC1242], -Data Rate of [RFC2544] and Intended Load of [RFC2285]. -All three specify this value applies to one (input or output) interface. - -### Trial Duration - -Trial duration is the intended duration of the traffic for a trial. - -In general, this quantity does not include any preparation nor waiting -described in section 23 of [RFC2544]. - -However, the measurer MAY return a duration value that deviates -from the intended duration. -This feature can be beneficial for users -who wish to manage the overall search duration, -rather than solely the traffic portion of it. -The manager MUST report -how the measurer computes the returned duration values in that case. - -### Trial Forwarding Ratio - -The trial forwarding ratio is a dimensionless floating point value -that ranges from 0.0 to 1.0, 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. - -Note that, contrary to loads, frame counts used to compute -trial forwarding ratio are aggregates over all SUT output ports. - -Questions around what is the correct number of frames -that should have been forwarded is outside of the scope of this document. -E.g. what should the measurer return when it detects -that the offered load differs significantly from the intended load. - -### Trial Loss Ratio - -The trial loss ratio is equal to one minus the trial forwarding ratio. - -### Trial Forwarding Rate - -The trial forwarding rate is a derived quantity, calculated by -multiplying the trial load by the trial forwarding ratio. - -It is important to note that while similar, this quantity is not identical -to the Forwarding Rate as defined in [RFC2285] section 3.6.1, -as the latter is specific to one output interface, -whereas the trial forwarding ratio is based -on frame counts aggregated over all SUT output interfaces. - -## Traffic profile - -Any other specifics (besides trial load and trial duration) -the measurer needs in order to perform the trial -are understood as a composite called the traffic profile. -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. - -The traffic profile is REQUIRED by [RFC2544] -to contain some specific quantities, for example frame size. -Several more specific quantities may be RECOMMENDED. - -Depending on SUT configuration, e.g. when testing specific protocols, -additional values need to be included in the traffic profile -and in the test report. -See other IETF documents. - -## Search Goal - -The search goal is a composite consisting of several attributes, -some of them are required. -Implementations are free to add their own attributes. - -A particular set of attribute values is called a search goal instance. - -Subsections list all required attributes and one recommended attribute. -Each subsection contains a short informal description, -but see other chapters for more in-depth explanations. - -The meaning of the attributes is formally given only by their effect -on the controller output attributes (defined in later in section Search Result). - -Informally, later chapters give additional intuitions and examples -to the search goal attribute values. -Later chapters also give motivation to formulas of computation of the outputs. - -### Goal Final Trial Duration - -A threshold value for trial durations. -This attribute is REQUIRED, and the value MUST be positive. - -Informally, while MLRsearch is allowed to perform trials shorter than this, -but results from such short trials have only limited impact on search results. - -The full relation needs definitions is later subsections. -But for example, the conditional throughput -(definition in subsection Conditional Throughput) -for this goal will be computed only from trial results -from trials at least as long as this. - -### Goal Duration Sum - -A threshold value for a particular sum of trial durations. -This attribute is REQUIRED, and the value MUST be positive. - -This uses the duration values returned by the measurer. - -Informally, even when looking only at trials done at this goal's -final trial duration, MLRsearch may spend up to this time measuring -the same load value. -If the goal duration sum is larger than -the goal final trial duration, it means multiple trials need to be measured -at the same load. - -### Goal Loss Ratio - -A threshold value for trial loss ratios. -REQUIRED attribute, MUST be non-negative and smaller than one. - -Informally, if a load causes too many trials with trial loss ratios -larger than this, the conditional throughput for this goal -will be smaller than that load. - -### Goal Exceed Ratio - -A threshold value for a particular ratio of duration sums. -REQUIRED attribute, MUST be non-negative and smaller than one. - -The duration sum values come from the duration values returned by the measurer. - -Informally, the impact of lossy trials is controlled by this value. -The full relation needs definitions is later subsections. - -But for example, the definition of the conditional throughput -(given later in subsection Conditional Throughput) -refers to a q-value for a quantile when selecting -which trial result gives the conditional throughput. -The goal exceed ratio acts as the q-value to use there. - -Specifically, when the goal exceed ratio is 0.5 and MLRsearch happened -to use the whole goal duration sum (using full-length trials), -it means the conditional throughput is the median of trial forwarding rates. - -### Goal Width - -A value used as a threshold for telling when two trial load values -are close enough. - -RECOMMENDED attribute, positive. -Implementations without this attribute -MUST give the manager other ways to control the search exit condition. - -Absolute load difference and relative load difference are two popular choices, -but implementations may choose a different way to specify width. - -Informally, this acts as a stopping condition, controlling the precision -of the search. -The search stops if every goal has reached its precision. - -## Controller Inputs - -The only REQUIRED input for controller is a set of search goal instances. -MLRsearch implementations MAY use additional input parameters for the controller. - -The order of instances SHOULD NOT have a big impact on controller outputs, -but MLRsearch implementations MAY base their behavior on the order -of search goal instances. - -The search goal instances SHOULD NOT be identical. -MLRsearch implementation MAY allow identical instances. - -## Goal Result - -Before defining the output of the controller, -it is useful to define what the goal result is. - -The goal result is a composite object consisting of several attributes. -A particular set of attribute values is called a goal result instance. - -Any goal result instance can be either regular or irregular. -MLRsearch specification puts requirements on regular goal result instances. -Any instance that does not meet the requirements is deemed irregular. - -Implementations are free to define their own irregular goal results, -but the manager MUST report them clearly as not regular according to this section. - -All attribute values in one goal result instance -are related to a single search goal instance, -referred to as the given search goal. - -Some of the attributes of a regular goal result instance are required, -some are recommended, implementations are free to add their own. - -The subsections define two required and one optional attribute -for a regular goal result. - -A typical irregular result is when all trials at the maximal offered load -have zero loss, as the relevant upper bound does not exist in that case. - -### Relevant Upper Bound - -The relevant upper bound is the smallest intended load value that is classified -at the end of the search as an upper bound (see Appendix A) -for the given search goal. -This is a REQUIRED attribute. - -Informally, this is the smallest intended load that failed to uphold -all the requirements of the given search goal, mainly the goal loss ratio -in combination with the goal exceed ratio. - -### Relevant Lower Bound - -The relevant lower bound is the largest intended 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) for the given search goal. -This is a REQUIRED attribute. - -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. - -Informally, this is the largest intended load that managed to uphold -all the requirements of the given search goal, mainly the goal loss ratio -in combination with the goal exceed ratio, while not being larger -than the relevant upper bound. - -### Conditional Throughput - -The conditional throughput (see Appendix B) -as evaluated at the relevant lower bound of the given search goal -at the end of the search. -This is a RECOMMENDED attribute. - -Informally, this is a typical forwarding rate expected to be seen -at the relevant lower bound of the given search goal. -But frequently just a conservative estimate thereof, -as MLRsearch implementations tend to stop gathering more data -as soon as they confirm the result cannot get worse than this estimate -within the goal duration sum. - -## Search Result - -The search result is a single composite object -that maps each search goal to a corresponding goal result. - -In other words, search result is an unordered list of key-value pairs, -where no two pairs contain equal keys. -The key is a search goal instance, acting as the given search goal -for the goal result instance in the value portion of the key-value pair. - -The search result (as a mapping) -MUST map from all the search goals present in the controller input. - -## Controller Outputs - -The search result is the only REQUIRED output -returned from the controller to the manager. - -MLRsearch implementation MAY return additional data in the controller output. - -# Further Explanations - -This chapter focuses on intuitions and motivations -and skips over some important details. - -Familiarity with the MLRsearch specification is not required here, -so this chapter can act as an introduction. -For example, this chapter starts talking about the tightest lower bounds -before it is ready to talk about the relevant lower bound from the specification. - -## MLRsearch Versions - -The MLRsearch algorithm has been developed in a code-first approach, -a Python library has been created, debugged, and used in production -before the first descriptions (even informal) were published. -In fact, multiple versions of the library were used in the production -over the past few years, and later code was usually not compatible -with earlier descriptions. - -The code in (any version of) MLRsearch library fully determines -the search process (for given configuration parameters), -leaving no space for deviations. -MLRsearch, as a name for a broad class of possible algorithms, -leaves plenty of space for future improvements, at the cost -of poor comparability of results of different MLRsearch implementations. - -There are two competing needs. -There is the need for standardization in areas critical to comparability. -There is also the need to allow flexibility for implementations -to innovate and improve in other areas. -This document defines the MLRsearch specification -in a manner that aims to fairly balances both needs. - -## Exit Condition - -[RFC2544] prescribes that after performing one trial at a specific offered load, -the next offered load should be larger or smaller, based on frame loss. - -The usual implementation uses binary search. -Here a lossy trial becomes -a new upper bound, a lossless trial becomes a new lower bound. -The span of values between (including both) the tightest lower bound -and the tightest upper bound forms an interval of possible results, -and after each trial the width of that interval halves. - -Usually the binary search implementation tracks only the two tightest bounds, -simply calling them bounds. -But the old values still B remain valid bounds, -just not as tight as the new ones. - -After some number of trials, the tightest lower bound becomes the throughput. -[RFC2544] does not specify when (if ever) should the search stop. - -MLRsearch library introduces a concept of goal width. -The search stops -when the distance between the tightest upper bound and the tightest lower bound -is smaller than a user-configured value, called goal width from now on. -In other words, the interval width at the end of the search -has to be no larger than the goal width. - -This goal width value therefore determines the precision of the result. -As MLRsearch specification requires a particular structure of the result, -the result itself does contain enough information to determine its precision, -thus it is not required to report the goal width value. - -This allows MLRsearch implementations to use exit conditions -different from goal width. - -## Load Classification - -MLRsearch keeps the basic logic of binary search (tracking tightest bounds, -measuring at the middle), perhaps with minor technical clarifications. -The algorithm chooses an intended load (as opposed to the offered load), -the interval between bounds does not need to be split -exactly into two equal halves, -and the final reported structure specifies both bounds. - -The biggest difference is that to classify a load -as an upper or lower bound, MLRsearch may need more than one trial -(depending on configuration options) to be performed at the same intended load. - -As a consequence, even if a load already does have few trial results, -it still may be classified as undecided, neither a lower bound nor an upper bound. - -An explanation of the classification logic is given in the next chapter, -as it relies heavily on other sections of this chapter. - -For repeatability and comparability reasons, it is important that -given a set of trial results, all implementations of MLRsearch -classify the load equivalently. - -## Loss Ratios - -The next difference is in the goals of the search. -[RFC2544] has a single goal, -based on classifying full-length trials as either lossless or lossy. - -As the name suggests, MLRsearch can search for multiple goals, -differing in their loss ratios. -The precise definition of the goal loss ratio will be given later. -The [RFC2544] throughput goal then simply becomes a zero goal loss ratio. -Different goals also may have different goal widths. - -A set of trial results for one specific intended load value -can classify the load as an upper bound for some goals, but a lower bound -for some other goals, and undecided for the rest of the goals. - -Therefore, the load classification depends not only on trial results, -but also on the goal. -The overall search procedure becomes more complicated -(compared to binary search with a single goal), -but most of the complications do not affect the final result, -except for one phenomenon, loss inversion. - -## Loss Inversion - -In [RFC2544] throughput search using bisection, any load with a lossy trial -becomes a hard upper bound, meaning every subsequent trial has a smaller -intended load. - -But in MLRsearch, a load that is classified as an upper bound for one goal -may still be a lower bound for another goal, and due to the other goal -MLRsearch will probably perform trials at even higher loads. -What to do when all such higher load trials happen to have zero loss? -Does it mean the earlier upper bound was not real? -Does it mean the later lossless trials are not considered a lower bound? -Surely we do not want to have an upper bound at a load smaller than a lower bound. - -MLRsearch is conservative in these situations. -The upper bound is considered real, and the lossless trials at higher loads -are considered to be a coincidence, at least when computing the final result. - -This is formalized using new notions, the relevant upper bound and -the relevant lower bound. -Load classification is still based just on the set of trial results -at a given intended load (trials at other loads are ignored), -making it possible to have a lower load classified as an upper bound, -and a higher load classified as a lower bound (for the same goal). -The relevant upper bound (for a goal) is the smallest load classified -as an upper bound. -But the relevant lower bound is not simply -the largest among lower bounds. -It is the largest load among loads -that are lower bounds while also being smaller than the relevant upper bound. - -With these definitions, the relevant lower bound is always smaller -than the relevant upper bound (if both exist), and the two relevant bounds -are used analogously as the two tightest bounds in the binary search. -When they are less than the goal width apart, -the relevant bounds are used in the output. - -One consequence is that every trial result can have an impact on the search result. -That means if your SUT (or your traffic generator) needs a warmup, -be sure to warm it up before starting the search. - -## Exceed Ratio - -The idea of performing multiple trials at the same load comes from -a model where some trial results (those with high loss) are affected -by infrequent effects, causing poor repeatability of [RFC2544] throughput results. -See the discussion about noiseful and noiseless ends -of the SUT performance spectrum. -Stable results are closer to the noiseless end of the SUT performance spectrum, -so MLRsearch may need to allow some frequency of high-loss trials -to ignore the rare but big effects near the noiseful end. - -MLRsearch can do such trial result filtering, but it needs -a configuration option to tell it how frequent can the infrequent big loss be. -This option is called the exceed ratio. -It tells MLRsearch what ratio of trials -(more exactly what ratio of trial seconds) can have a trial loss ratio -larger than the goal loss ratio and still be classified as a lower bound. -Zero exceed ratio means all trials have to have a trial loss ratio -equal to or smaller than the goal loss ratio. - -For explainability reasons, the RECOMMENDED value for exceed ratio is 0.5, -as it simplifies some later concepts by relating them to the concept of median. - -## Duration Sum - -When more than one trial is needed to classify a load, -MLRsearch also needs something that controls the number of trials needed. -Therefore, each goal also has an attribute called duration sum. - -The meaning of a goal duration sum is that when a load has trials -(at full trial duration, details later) -whose trial durations when summed up give a value at least this long, -the load is guaranteed to be classified as an upper bound or a lower bound -for the goal. - -As the duration sum has a big impact on the overall search duration, -and [RFC2544] prescribes wait intervals around trial traffic, -the MLRsearch algorithm is allowed to sum durations that are different -from the actual trial traffic durations. - -## Short Trials - -MLRsearch requires each goal to specify its final trial duration. -Full-length trial is a shorter name for a trial whose intended trial duration -is equal to (or longer than) the goal final trial duration. - -Section 24 of [RFC2544] already anticipates possible time savings -when short trials (shorter than full-length trials) are used. -Full-length trials are the opposite of short trials, -so they may also be called long trials. - -Any MLRsearch implementation may include its own configuration options -which control when and how MLRsearch chooses to use shorter trial durations. - -For explainability reasons, when exceed ratio of 0.5 is used, -it is recommended for the goal duration sum to be an odd multiple -of the full trial durations, so conditional throughput becomes identical to -a median of a particular set of forwarding rates. - -The presence of shorter trial results complicates the load classification logic. -Full details are given later. -In short, 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). - -For explainability reasons, it is RECOMMENDED users use such configurations -that guarantee all trials have the same length. -Alas, such configurations are usually not compliant with [RFC2544] requirements, -or not time-saving enough. - -## Conditional Throughput - -As testing equipment takes the intended load as an input parameter -for a trial measurement, any load search algorithm needs to deal -with intended load values internally. - -But in the presence of goals with a non-zero loss ratio, the intended load -usually does not match the user's intuition of what a throughput is. -The forwarding rate (as defined in [RFC2285] section 3.6.1) is better, -but it is not obvious how to generalize it -for loads with multiple trial results and a non-zero goal loss ratio. - -MLRsearch defines one such generalization, called the conditional throughput. -It is the forwarding rate from one of the trials performed at the load -in question. -Specification of which trial exactly is quite technical, -see the specification and Appendix B. - -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, -and guaranteed to show an effective loss ratio -no larger than the goal loss ratio. - -While the conditional throughput gives more intuitive-looking values -than the relevant lower bound, especially for non-zero goal loss ratio values, -the actual definition is more complicated than the definition of the relevant -lower bound. -In the future, other intuitive values may become popular, -but they are unlikely to supersede the definition of the relevant lower bound -as the most fitting value for comparability purposes, -therefore the relevant lower bound remains a required attribute -of the goal result structure, while the conditional throughput is only optional. - -Note that comparing the best and worst case, the same relevant lower bound value -may result in the conditional throughput differing up to the goal loss ratio. -Therefore it is rarely needed to set the goal width (if expressed -as the relative difference of loads) below the goal loss ratio. -In other words, setting the goal width below the goal loss ratio -may cause the conditional throughput for a larger loss ratio to become smaller -than a conditional throughput for a goal with a smaller goal loss ratio, -which is counter-intuitive, considering they come from the same search. -Therefore it is RECOMMENDED to set the goal width to a value no smaller -than the goal loss ratio. - -## Search Time - -MLRsearch was primarily developed to reduce the time -required to determine a throughput, either the [RFC2544] compliant one, -or some generalization thereof. -The art of achieving short search times -is mainly in the smart selection of intended loads (and intended durations) -for the next trial to perform. - -While there is an indirect impact of the load selection on the reported values, -in practice such impact tends to be small, -even for SUTs with quite a broad performance spectrum. - -A typical example of two approaches to load selection leading to different -relevant lower bounds is when the interval is split in a very uneven way. -Any implementation choosing loads very close to the current relevant lower bound -is quite likely to eventually stumble upon a trial result -with poor performance (due to SUT noise). -For an implementation choosing loads very close -to the current relevant upper bound, this is unlikely, -as it examines more loads that can see a performance -close to the noiseless end of the SUT performance spectrum. - -However, as even splits optimize search duration at give precision, -MLRsearch implementations that prioritize minimizing search time -are unlikely to suffer from any such bias. - -Therefore, this document remains quite vague on load selection -and other optimization details, and configuration attributes related to them. -Assuming users prefer libraries that achieve short overall search time, -the definition of the relevant lower bound -should be strict enough to ensure result repeatability -and comparability between different implementations, -while not restricting future implementations much. - -Sadly, different implementations may exhibit their sweet spot of -the best repeatability for a given search duration -at different goals attribute values, especially concerning -any optional goal attributes such as the initial trial duration. -Thus, this document does not comment much on which configurations -are good for comparability between different implementations. -For comparability between different SUTs using the same implementation, -refer to configurations recommended by that particular implementation. - -## [RFC2544] compliance - -The following search goal ensures unconditional compliance with -[RFC2544] throughput search procedure: - -- Goal loss ratio: zero. - -- Goal final trial duration: 60 seconds. - -- Goal duration sum: 60 seconds. - -- Goal exceed ratio: zero. - -The presence of other search goals does not affect the compliance -of this goal result. -The relevant lower bound and the conditional throughput are in this case -equal to each other, and the value is the [RFC2544] throughput. - -If the 60 second quantity is replaced by a smaller quantity in both attributes, -the conditional throughput is still conditionally compliant with -[RFC2544] throughput. - -# Logic of Load Classification - -This chapter continues with explanations, -but this time more precise definitions are needed -for readers to follow the explanations. -The definitions here are wordy, implementers should read the specification -chapter and appendices for more concise definitions. - -The two related areas of focus in this chapter are load classification -and the conditional throughput, starting with the latter. - -The section Performance Spectrum contains definitions -needed to gain insight into what conditional throughput means. -The rest of the subsections discuss load classification, -they do not refer to Performance Spectrum, only to a few duration sums. - -For load classification, it is useful to define good and bad trials. -A trial is called bad (according to a goal) if its trial loss ratio -is larger than the goal loss ratio. -The trial that is not bad is called good. - -## Performance Spectrum - -There are several equivalent ways to explain -the conditional throughput computation. -One of the ways relies on an object called the performance spectrum. -First, two heavy definitions are needed. - -Take an intended load value, a trial duration value, and a finite set -of trial results, all trials measured at that load value and duration value. -The performance spectrum is the function that maps -any non-negative real number into a sum of trial durations among all trials -in the set that has that number as their forwarding rate, -e.g. map to zero if no trial has that particular forwarding rate. - -A related function, defined if there is at least one trial in the set, -is the performance spectrum divided by the sum of the durations -of all trials in the set. -That function is called the performance probability function, as it satisfies -all the requirements for probability mass function function -of a discrete probability distribution, -the one-dimensional random variable being the trial forwarding rate. - -These functions are related to the SUT performance spectrum, -as sampled by the trials in the set. - -As for any other probability function, we can talk about percentiles -of the performance probability function, including the median. -The conditional throughput will be one such quantile value -for a specifically chosen set of trials. - -Take a set of all full-length trials performed at the relevant lower bound, -sorted by decreasing 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 forwarding rate, -such that the new sum of durations is equal to the goal duration sum. -This is the set of trials to use. -The q-value for the quantile -is the goal exceed ratio. -If the quantile touches two trials, -the larger forwarding rate (from the trial result sorted earlier) is used. -The resulting quantity is the conditional throughput of the goal in question. - -First example. -For zero exceed ratio, when goal duration sum has been reached. -The conditional throughput is the smallest forwarding rate among the trials. - -Second example. -For zero exceed ratio, when goal duration sum has not been reached yet. -Due to the missing duration sum, the worst case may still happen, -so the conditional throughput is zero. -This is not reported to the user, -as this load cannot become the relevant lower bound yet. - -Third example. -Exceed ratio 50%, goal duration sum two seconds, -one trial present with the duration of one second and zero loss. -The imaginary trial is added with the duration -of one second and zero forwarding rate. -The median would touch both trials, so the conditional throughput -is the forwarding rate of the one non-imaginary trial. -As that had zero loss, the value is equal to the offered load. - -Note that Appendix B does not take into account short trial results. - -### Summary - -While the conditional throughput is a generalization of the forwarding rate, -its definition is not an obvious one. - -Other than the forwarding rate, the other source of intuition -is the quantile in general, and the median the the recommended case. - -In future, different quantities may prove more useful, -especially when applying to specific problems, -but currently the conditional throughput is the recommended compromise, -especially for repeatability and comparability reasons. - -## Single Trial Duration - -When goal attributes are chosen in such a way that every trial has the same -intended duration, the load classification is simpler. - -The following description looks technical, but it follows the motivation -of goal loss ratio, goal exceed ratio, and goal duration sum. -If the sum of the durations of all trials (at the given load) -is less than the goal duration sum, imagine best case scenario -(all subsequent trials having zero loss) and worst case scenario -(all subsequent trials having 100% loss). -Here we assume there are as many subsequent trials as needed -to make the sum of all trials equal to the goal duration sum. -As the exceed ratio is defined just using sums of durations -(number of trials does not matter), it does not matter whether -the "subsequent trials" can consist of an integer number of full-length trials. - -In any of the two scenarios, 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. - -If even in the best case scenario the load exceed ratio would be larger -than the goal exceed ratio, the load is an upper bound. -If even in the worst case scenario the load exceed ratio would not be larger -than the goal exceed ratio, the load is a lower bound. - -Even more specifically. -Take all trials measured at a given load. -The sum of the durations of all bad full-length trials is called the bad sum. -The sum of the durations of all good full-length trials is called the good sum. -The result of adding the bad sum plus the good sum is called the measured sum. -The larger of the measured sum and the goal duration sum is called the whole sum. -The whole sum minus the measured sum is called the missing sum. -The optimistic exceed ratio is the bad sum divided by the whole sum. -The pessimistic exceed ratio is the bad sum plus the missing sum, -that divided by the whole sum. -If the optimistic exceed ratio is larger than the goal exceed ratio, -the load is classified as an upper bound. -If the pessimistic exceed ratio is not larger than the goal exceed ratio, -the load is classified as a lower bound. -Else, the load is classified as undecided. - -The definition of pessimistic exceed ratio is compatible with the logic in -the conditional throughput computation, so in this single trial duration case, -a load is a lower bound if and only if the conditional throughput -effective loss ratio is not larger than the goal loss ratio. -If it is larger, the load is either an upper bound or undecided. - -## Short Trial Scenarios - -Trials with intended duration smaller than the goal final trial duration -are called short trials. -The motivation for load classification logic in the presence of short trials -is based around a counter-factual case: What would the trial result be -if a short trial has been measured as a full-length trial instead? - -There are three main scenarios where human intuition guides -the intended behavior of load classification. - -False good scenario. -The user had their reason for not configuring a shorter goal -final trial duration. -Perhaps SUT has buffers that may get full at longer -trial durations. -Perhaps SUT shows periodic decreases in performance -the user does not want to be treated as noise. -In any case, many good short trials may become bad full-length trials -in the counter-factual case. -In extreme cases, there are plenty of good short trials and no bad short trials. -In this scenario, we want the load classification NOT to classify the load -as a lower bound, despite the abundance of good short trials. -Effectively, we want the good short trials to be ignored, so they -do not contribute to comparisons with the goal duration sum. - -True bad scenario. -When there is a frame loss in a short trial, -the counter-factual full-length trial is expected to lose at least as many -frames. -And in practice, bad short trials are rarely turning into -good full-length trials. -In extreme cases, there are no good short trials. -In this scenario, we want the load classification -to classify the load as an upper bound just based on the abundance -of short bad trials. -Effectively, we want the bad short trials -to contribute to comparisons with the goal duration sum, -so the load can be classified sooner. - -Balanced scenario. -Some SUTs are quite indifferent to trial duration. -Performance probability function constructed from short trial results -is likely to be similar to the performance probability function constructed -from full-length trial results (perhaps with larger dispersion, -but without a big impact on the median quantiles overall). -For a moderate goal exceed ratio value, this may mean there are both -good short trials and bad short trials. -This scenario is there just to invalidate a simple heuristic -of always ignoring good short trials and never ignoring bad short trials. -That simple heuristic would be too biased. -Yes, the short bad trials -are likely to turn into full-length bad trials in the counter-factual case, -but there is no information on what would the good short trials turn into. -The only way to decide safely is to do more trials at full length, -the same as in scenario one. - -## Short Trial Logic - -MLRsearch picks a particular logic for load classification -in the presence of short trials, but it is still RECOMMENDED -to use configurations that imply no short trials, -so the possible inefficiencies in that logic -do not affect the result, and the result has better explainability. - -With that said, the logic differs from the single trial duration case -only in different definition of the bad sum. -The good sum is still the sum across all good full-length trials. - -Few more notions are needed for defining the new bad sum. -The sum of durations of all bad full-length trials is called the bad long sum. -The sum of durations of all bad short trials is called the bad short sum. -The sum of durations of all good short trials is called the good short sum. -One minus the goal exceed ratio is called the inceed ratio. -The goal exceed ratio divided by the inceed ratio is called the exceed coefficient. -The good short sum multiplied by the exceed coefficient is called the balancing sum. -The bad short sum minus the balancing sum is called the excess sum. -If the excess sum is negative, the bad sum is equal to the bad long sum. -Otherwise, the bad sum is equal to the bad long sum plus the excess sum. - -Here is how the new definition of the bad sum fares in the three scenarios, -where the load is close to what would the relevant bounds be -if only full-length trials were used for the search. - -False good scenario. -If the duration is too short, we expect to see a higher frequency -of good short trials. -This could lead to a negative excess sum, -which has no impact, hence the load classification is given just by -full-length trials. -Thus, MLRsearch using too short trials has no detrimental effect -on result comparability in this scenario. -But also using short trials does not help with overall search duration, -probably making it worse. - -True bad cenario. -Settings with a small exceed ratio -have a small exceed coefficient, so the impact of the good short sum is small, -and the bad short sum is almost wholly converted into excess sum, -thus bad short trials have almost as big an impact as full-length bad trials. -The same conclusion applies to moderate exceed ratio values -when the good short sum is small. -Thus, short trials can cause a load to get classified as an upper bound earlier, -bringing time savings (while not affecting comparability). - -Balanced scenario. -Here excess sum is small in absolute value, as the balancing sum -is expected to be similar to the bad short sum. -Once again, full-length trials are needed for final load classification; -but usage of short trials probably means MLRsearch needed -a shorter overall search time before selecting this load for measurement, -thus bringing time savings (while not affecting comparability). - -Note that in presence of short trial results, -the comparibility between the load classification -and the conditional throughput is only partial. -The conditional throughput still comes from a good long trial, -but a load higher than the relevant lower bound may also compute to a good value. - -## Longer Trial Durations - -If there are trial results with an intended duration larger -than the goal trial duration, the precise definitions -in Appendix A and Appendix B treat them in exactly the same way -as trials with duration equal to the goal trial duration. - -But in configurations with moderate (including 0.5) or small -goal exceed ratio and small goal loss ratio (especially zero), -bad trials with longer than goal durations may bias the search -towards the lower load values, as the noiseful end of the spectrum -gets a larger probability of causing the loss within the longer trials. - -For some users, this is an acceptable price -for increased configuration flexibility -(perhaps saving time for the related goals), -so implementations SHOULD allow such configurations. -Still, users are encouraged to avoid such configurations -by making all goals use the same final trial duration, -so their results remain comparable across implementations. - -# Addressed Problems - -Now when MLRsearch is clearly specified and explained, -it is possible to summarize how does MLRsearch specification help with problems. - -Here, "multiple trials" is a shorthand for having the goal final trial duration -significantly smaller than the goal duration sum. -This results in MLRsearch performing multiple trials at the same load, -which may not be the case with other configurations. - -## Long Test Duration - -As shortening the overall search duration is the main motivation -of MLRsearch library development, the library implements -multiple improvements on this front, both big and small. - -Most of implementation details are not constrained by the MLRsearch specification, -so that future implementations may keep shortening the search duration even more. - -One exception is the impact of short trial results on the relevant lower bound. -While motivated by human intuition, the logic is not straightforward. -In practice, configurations with only one common trial duration value -are capable of achieving good overal search time and result repeatability -without the need to consider short trials. - -### Impact of goal attribute values - -From the required goal attributes, the goal duration sum -remains the best way to get even shorter searches. - -Usage of multiple trials can also save time, -depending on wait times around trial traffic. - -The farther the goal exceed ratio is from 0.5 (towards zero or one), -the less predictable the overal search duration becomes in practice. - -Width parameter does not change search duration much in practice -(compared to other, mainly optional goal attributes). - -## DUT in SUT - -In practice, using multiple trials and moderate exceed ratios -often improves result repeatability without increasing the overall search time, -depending on the specific SUT and DUT characteristics. -Benefits for separating SUT noise are less clear though, -as it is not easy to distinguish SUT noise from DUT instability in general. - -Conditional throughput has an intuitive meaning when described -using the performance spectrum, so this is an improvement -over existing simple (less configurable) search procedures. - -Multiple trials can save time also when the noisy end of -the preformance spectrum needs to be examined, e.g. for [RFC9004]. - -Under some circumstances, testing the same DUT and SUT setup with different -DUT configurations can give some hints on what part of noise is SUT noise -and what part is DUT performance fluctuations. -In practice, both types of noise tend to be too complicated for that analysis. - -MLRsearch enables users to search for multiple goals, -potentially providing more insight at the cost of a longer overall search time. -However, for a thorough and reliable examination of DUT-SUT interactions, -it is necessary to employ additional methods beyond black-box benchmarking, -such as collecting and analyzing DUT and SUT telemetry. - -## Repeatability and Comparability - -Multiple trials improve repeatability, depending on exceed ratio. - -In practice, one-second goal final trial duration with exceed ratio 0.5 -is good enough for modern SUTs. -However, unless smaller wait times around the traffic part of the trial -are allowed, too much of overal search time would be wasted on waiting. - -It is not clear whether exceed ratios higher than 0.5 are better -for repeatability. -The 0.5 value is still preferred due to explainability using median. - -It is possible that the conditional throughput values (with non-zero goal -loss ratio) are better for repeatability than the relevant lower bound values. -This is especially for implementations -which pick load from a small set of discrete values, -as that hides small variances in relevant lower bound values -other implementations may find. - -Implementations focusing on shortening the overall search time -are automatically forced to avoid comparability issues due to load selection, -as they must prefer even splits wherever possible. -But this conclusion only holds when the same goals are used. -Larger adoption is needed before any further claims on comparability -between MLRsearch implementations can be made. - -## Throughput with Non-Zero Loss - -Trivially suported by the goal loss ratio attribute. - -In practice, usage of non-zero loss ratio values -improves the result repeatability -(exactly as expected based on results from simpler search methods). - -## Inconsistent Trial Results - -MLRsearch is conservative wherever possible. -This is built into the definition of conditional throughput, -and into the treatment of short trial results for load classification. - -This is consistent with [RFC2544] zero loss tolerance motivation. - -If the noiseless part of the SUT performance spectrum is of interest, -it should be enough to set small value for the goal final trial duration, -and perhaps also a large value for the goal exceed ratio. - -Implementations may offer other (optional) configuration attributes -to become less conservative, but currently it is not clear -what impact would that have on repeatability. - -# IANA Considerations - -No requests of IANA. - -# Security Considerations - -Benchmarking activities as described in this memo are limited to -technology characterization of a DUT/SUT using controlled stimuli in a -laboratory environment, with dedicated address space and the constraints -specified in the sections above. - -The benchmarking network topology will be an independent test setup and -MUST NOT be connected to devices that may forward the test traffic into -a production network or misroute traffic to the test management network. - -Further, benchmarking is performed on a "black-box" basis, relying -solely on measurements observable external to the DUT/SUT. - -Special capabilities SHOULD NOT exist in the DUT/SUT specifically for -benchmarking purposes. Any implications for network security arising -from the DUT/SUT SHOULD be identical in the lab and in production -networks. - -# Acknowledgements - -Some phrases and statements in this document were created -with help of Mistral AI (mistral.ai). - -Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough -review and numerous useful comments and suggestions. - -Special wholehearted gratitude and thanks to the late Al Morton for his -thorough reviews filled with very specific feedback and constructive -guidelines. Thank you Al for the close collaboration over the years, -for your continuous unwavering encouragement full of empathy and -positive attitude. -Al, you are dearly missed. - -# Appendix A: Load Classification - -This is the specification of how to perform the load classification. - -Any intended load value can be classified, according to the given search goal. - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the measurer. - -The block at the end of this appendix holds pseudocode -which computes two values, stored in variables named optimistic and pessimistic. -The pseudocode happens to be a valid Python code. - -If both values are computed to be true, the load in question -is classified as a lower bound according to the given search goal. -If both values are false, the load is classified as an upper bound. -Otherwise, the load is classified as undecided. - -The pseudocode expects the following variables to hold values as follows: - -- goal_duration_sum: The duration sum value of the given search goal. - -- goal_exceed_ratio: The exceed ratio value of the given search goal. - -- good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a trial loss ratio - not higher than the goal loss ratio. - -- bad_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a trial loss ratio - higher than the goal loss ratio. - -- good_short_sum: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a trial loss ratio - not higher than the goal loss ratio. - -- bad_short_sum: Sum of durations across trials with trial duration - shorter than the goal final trial duration and with a trial loss ratio - higher than the goal loss ratio. - -The code works correctly also when there are no trial results at the given load. - -~~~ python -balancing_sum = good_short_sum * goal_exceed_ratio / (1.0 - goal_exceed_ratio) -effective_bad_sum = bad_long_sum + max(0.0, bad_short_sum - balancing_sum) -effective_whole_sum = max(good_long_sum + effective_bad_sum, goal_duration_sum) -quantile_duration_sum = effective_whole_sum * goal_exceed_ratio -optimistic = effective_bad_sum <= quantile_duration_sum -pessimistic = (effective_whole_sum - good_long_sum) <= quantile_duration_sum -~~~ - -# Appendix B: Conditional Throughput - -This is the specification of how to compute conditional throughput. - -Any intended load value can be used as the basis for the following computation, -but only the relevant lower bound (at the end of the search) -leads to the value called the conditional throughput for a given search goal. - -The algorithm uses (some subsets of) the set of all available trial results -from trials measured at a given intended load at the end of the search. -All durations are those returned by the measurer. - -The block at the end of this appendix holds pseudocode -which computes a value stored as variable conditional_throughput. -The pseudocode happens to be a valid Python code. - -The pseudocode expects the following variables to hold values as follows: - -- goal_duration_sum: The duration sum value of the given search goal. - -- goal_exceed_ratio: The exceed ratio value of the given search goal. - -- good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a trial loss ratio - not higher than the goal loss ratio. - -- bad_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with a trial loss ratio - higher than the goal loss ratio. - -- long_trials: An iterable of all trial results from trials with trial duration - at least equal to the goal final trial duration, - sorted by increasing the trial loss ratio. - A trial result is a composite with the following two attributes available: - - - trial.loss_ratio: The trial loss ratio as measured for this trial. - - - trial.duration: The trial duration of this trial. - -The code works correctly only when there if there is at least one -trial result measured at a given load. - -~~~ python -all_long_sum = max(goal_duration_sum, good_long_sum + bad_long_sum) -remaining = all_long_sum * (1.0 - goal_exceed_ratio) -quantile_loss_ratio = None -for trial in long_trials: - if quantile_loss_ratio is None or remaining > 0.0: - quantile_loss_ratio = trial.loss_ratio - remaining -= trial.duration - else: - break -else: - if remaining > 0.0: - quantile_loss_ratio = 1.0 -conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) -~~~ - ---- back |