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diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-05.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-05.md deleted file mode 100644 index 937e632413..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-05.md +++ /dev/null @@ -1,1460 +0,0 @@ ---- -title: Multiple Loss Ratio Search -abbrev: MLRsearch -docname: draft-ietf-bmwg-mlrsearch-05 -date: 2023-10-23 - -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/0.4.1/ - title: "MLRsearch 0.4.1, Python Package Index" - date: 2021-07 - ---- abstract - -This document proposes extensions to [RFC2544] throughput search by -defining a new methodology called Multiple Loss Ratio search -(MLRsearch). The main objectives of MLRsearch are to minimize the -total search duration, to support searching for multiple loss ratios -and to improve results repeatability and comparability. - -The main motivation behind extending [RFC2544] is the new set of challenges -and requirements posed by evaluating and testing software based networking -systems, specifically their data planes. - -MLRsearch offers several ways to address these challenges, giving user -configuration options to select their preferred way. - ---- middle - -{::comment} - As we use kramdown to convert from markdown, - we use this way of marking comments not to be visible in rendered draft. - https://stackoverflow.com/a/42323390 - If other 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 a number of problems: - -- Binary search takes too long as most of 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 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. - -MLRsearch library aims to address these problems by applying the following set -of enhancements: - -- Allow multiple shorter trials instead of one big trial per load. - - Optionally, tolerate a percentage of trial result 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 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 preformance testing methodologies, -mainly a binary search for [RFC2544] unconditionally compliant throughput. - -The last chapter will summarize how the problems are addressed, -the middle chapters provide explanations and definitions needed for that. - -## Long Search Duration - -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 requred to verify DUT update -and the frequency of running those tests. -This makes the overall test execution time even more important than before. - -In the context of characterising particular DUT's network performance, -this calls for improving the time efficiency of throughput search. -A vanilla bisection (at 60sec trial duration for unconditional [RFC2544] -compliance) is slow, because most trials spend time quite far from the -eventual throughput. - -[RFC2544] does not specify any stopping condition for throughput search, -so users can trade-off between search duration and achieved precision. -But, due to logarithmic nature of bisection, even small improvement -in search duration needs relatively big sacrifice in the precision of the -discovered throughput. - -## 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 case of software networking, the SUT consists nt only of 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. - -The DUT is effectively nested within the rest of the SUT. - -Due to a shared multi-tenant nature of SUT, DUT is subject to -possible interference coming from the operating system and any other -software running on the same server. Some sources of such interference -can be to some degree eliminated, e.g. by pinning DUT program threads -to specific CPU cores and isolating those cores to avoid context switching. -But some level of adverse effects may remain even after -all such reasonable precautions are applied. -These effects affect DUT's network performance negatively. -As the effects are hard to predict in general, they have impact similar to -what other engineering disciplines define as a noise. -Thus, all such effects are called an SUT 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 destinguish, -this document uses the word noise as a shorthand covering both -this internal DUT performance fluctuations and genuine SUT noise. - -A simple model of SUT performance consists of an idealized noiseless performance, -and additional noise effects. The noiseless performance is assumed to be constant, -all observed performance variations are due 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 results -close to the noiseful end of the spectrum happen only rarely. -The worse the performance value is, the more rarely it is seen in a trial. -Therefore, the extreme noiseful end of SUT spectrum is not really observable -among trial results. Also, the extreme noiseless end of 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 talks about potentially observable -ends of the SUT performance spectrum, not about the extreme ones. - -Focusing on DUT, the benchmarking effort should aim -at eliminating only the SUT noise from SUT measurements. -In practice that is not really possible, as based on authors experience -and available literature, there are no realistic enough models -able to distinguish SUT noise from DUT fluctuations. - -However, assuming that a well-constructed SUT has the DUT as its -performance bottleneck, the DUT ideal noiseless performance can be defined -as the noiseless end of SUT performance spectrum. At least for -throughput. For other performance quantities such as latency there may be an -additive difference. - -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. - -In this document, we reduce the DUT in SUT problem to estimating -the noiseless end of SUT performance spectrum from a limited number of -trial results. - -Any improvements to throughput search algorithm, aimed for better -dealing with software networking SUT and DUT setup, should employ -strategies recognizing the presence of SUT noise, and allow 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. -In practice, poor repeatability is also the main cause of poor -comparability, that is different benchmarking teams can test the same SUT -but get throughput values differing more than expected 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 resulting in poor throughput repeatability. - -The repeatability problem can be addressed by defining a search procedure -which reports more stable results, -even if they can no longer be called throughput in [RFC2544] sense. -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. - -Contrary to that, many benchmarking teams settle with small, non-zero -loss ratio as the goal for a 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 small SUT performance fluctuatios - is enough to cause frame loss. - -- Small bursts of frame loss caused by noise have otherwise smaller impact - on the average frame loss ratio ovserved in the trial, - as during other parts of the same trial the SUT may work mroe 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 validity of any and 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. - -Assuming users are allowed to specify the goal loss ratio value, -the usefulness is enhanced even more if users can specify multiple -loss ratio values, especially when a single search can find all relevant bounds. - -Searching for multiple search goals also helps to describe the SUT performance -spectrum better than a single search goal result. -For example, 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 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 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 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 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 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 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 repeatibility enhancements), -while being precise enough to force a specific way to resolve trial result -inconsistencies. -But until such 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. -Presence of other components (mainly the SUT) is also implied. - -While the manager and the measurer can be seen a abstractions -present in any testing procedure, the behavior of the controller -is what distinguishes MLRsearch algorithms from other search procedures. - -### Measurer - -The measurer is the component which performs one trial -as described in [RFC2544] section 23, when requested by the controller. - -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 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 tester sent zero frames towards SUT. -Implementations are RECOMMENDED to document their behavior -related to such freedoms in as detailed way as possible - -Implemenations 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 is the component of MLRsearch architecture -that is called by the manager (just once), calls the measurer -(usually multiple times in succession), -and returns the result of the search to the manager. - -The only required argument in the call to the controller -is a list of search goals. For the structure of the search result, -see subsection Search Result. - -### Manager - -The manager is the component that initializes SUT, traffic generator -(tester in [RFC2544]), the measurer and the controller -with intended configurations. It then hands over the execution -to the controller and receives its result. - -Creation of reports of appropriate format can also be understood -as the responsibility of the manager. - -## 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 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 test described in [RFC2544] section 23. - -### Trial Load - -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 general quantity does not include any preparation nor waiting -described in section 23 of [RFC2544]. - -However, the measurer MAY return a duration value different -from the intended duration. This may be useful for users -who want to control the overal search duration, not just the traffic part of it. -The manager MUST report how does the measurer computes the returned duration -values in that case. - -### Trial Forwarding Ratio - -Trial forwarding ratio is dimensionless floating point value, -assumed to be between 0.0 and 1.0, both including. -It is computed as the number of frames forwarded by SUT, divided by -the number of frames that should have been forwarded during the trial. - -Note that, contrary to load, frame counts used to compute -trial forwarding ratio are aggregates over all SUT ports. - -Questions around what is the correct number of frames -that should have been forwarded it 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. - -It is RECOMMENDED implementations return an irregular goal result -if they detect questionable (in comparability sense) trial results -affecting their goal result. - -### Trial Loss Ratio - -Trial loss ratio is equal to one minus the trial forwarding ratio. - -### Trial Forwarding Rate - -The trial forwarding rate is the trial load multiplied by -the trial forwarding ratio. - -Note that this is very similar, but not identical to Forwarding Rate -as defined in [RFC2285] section 3.6.1, as that definition -is specific to one output interface, while trial forwarding ratio -is based on frame counts aggregated over all SUT interfaces. - -## Traffic profile - -Any other specifics (besides trial load and trial duration) -the measurer needs 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. - -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 - -A search goal is one item of the list required as an argument -when the manager calls the controller. - -Each search goal is composite consisting of several attributes, -some of them are required. -Implementations are free to add their own attributes. - -Subsections list all required attributes and one recommended attribute. - -The meaning of the attributes is formally given only by their effect -on the computation of the attributes of the goal result. -The subsections do contain a short informal description, -but see other chapters for more in-depth explanations. - -### Goal Final Trial Duration - -A threshold value for trial durations. -REQUIRED attribute, MUST be positive. - -Informally, the conditional throughput for this goal will be computed -only from trial results from trials as long as this. - -### Goal Duration Sum - -A threshold value for a particular sum of trial durations. -REQUIRED attribute, MUST be positive. - -This uses the duration values returned by the measurer, -in case it returns something else than the intended durations -for the traffic part of the search. - -Informally, even at looking only at trials done at this goal's -final trial duration, MLRsearch may spend up to this time measuring -the same load value. - -### 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 results larger than this, -the conditional throughput for this goal will be smaller than that load. - -### Goal Exceed Ratio - -A threshold value for particular ratio of duration sums. -REQUIRED attribute, MUST be non-negative and smaller than one. - -This uses the duration values returned by the measurer, -in case it returns something else than the intended durations -for the traffic part of the search. - -Informally, this acts as the q-value for a quantile when selecting -the forwarding rate of which trial result becomes the conditional throughput. -For example, when the goal exceed ratio is 0.5 and MLRsearch -happened to use the whole goal duration sum when determining conditional -throughput, it means the conditional throughput is the median -of trial forwarding rates. -In practice, MLRsearch may stop measuring a load before the goal duration sum -is reached, and the conditional throughput in that case frequently -is the worst trial still not exceeding the goal loss ratio. -If the goal duration sum is no larger than the goal fina trial duration, -MLRsearch performs only one trial per load (unless other goals need more) -and the goal exceed ratio has no effect on the search result. - -### 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, controling the precision -of the search. The search stops if every goal has reached its precision. - -## Search Result - -Search result is a single composite object returned from the controller -to the manager. -It is a mapping from the search goals (see section Search Goal) into goal results -(see section Goal Result). -The mapping MUST map from all the search goals present in the controller input. - -Each search goal instance is mapped to a goal result instance. -Multiple search goal instances may map to the same goal result instance. - -## Goal Result - -Goal result is a composite object consisting of several attributes. -All attributes are related to the same search goal, the one search goal instance -the Search Result is mapping into this instance of the Goal Result. - -Some of the attributes are required, some are recommended, -implementations are free to add their own. - -The subsections define attributes for regular goal result. -Implementations are free to define their own irregular goal results, -but the manager MUST report them clearly as not regular according to this section. - -A typical irregular result is when all trials at maximum offered load -have zero loss, as the relevant upper bound does not exist in that case. - -### Relevant Upper Bound - -Relevant upper bound is the intended load value that is classified -at the end of the search as the relevant upper bound (see Appendix A) -for this goal. -This is a REQUIRED attribute. - -Informally, this is the smallest intended load that failed to uphold -all the requirements of this search goal, mainly the goal loss ratio -in combination with the goal exceed ratio. - -### Relevant Lower Bound - -Relevant lower bound is the intended load value that got classified -(after all trials) as the relevant lower bound (see Appendix A) for this goal. -This is a REQUIRED attribute. - -The distance between the relevant lower bound and the relevant upper bound -MUST NOT be larger than the goal width, for a regular goal result, -if the implementation offers width as a goal attribute. - -Informally, this is the smallest intended load that managed to uphold -all the requirements of this 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. -This is a RECOMMENDED attribute. - -Informally, a typical forwarding rate expected to be seen -at the relevant lower bound. But frequently just a conservative estimate thereof, -as MLRsearch implementations tend to stop gathering more data -as soon as they confirm this estimate cannot get worse within -the goal duration sum. - -# MLRsearch 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 start talking about 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 first descriptions (even informal) were published. -In fact, multiple versions of the library were used in production -over 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. - -This document aspires to prescribe a MLRsearch specification -in a way that restricts the important parts related to comparability, -while leaving other parts vague enough so implementations can improve freely. - -## 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 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 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, interval width has to be smaller than goal width -at the end of the search. - -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. -The MLRsearch specification only REQUIRES the search procedure -to always finish in a finite time, regardless of possible trial results. - -## 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 offered load), -the interval between bounds does not need to be split exactly in two equal halves, -and the final reported structure specifies both bounds -(optionally also the conditional throughput at the lower bound, defined later). - -The biggest difference is that in order 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. - -Explanation of the classification logic is given in the next chatper, -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 in an equivalent way. - -## Loss Ratios - -Next difference is in goals of the search. [RFC2544] has a single goal, -based on classifying full-length trials as either loss-less or lossy. - -As the name suggests, MLRsearch can seach for multiple goals, differing in their -loss ratios. Precise definition of 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 width. - -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 ttrial 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] throuhput search using bisection, any load with lossy trial -becomes a hard upper bound, meaning every subsequent trial has 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 that 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 goal width apart, the relevant bounds are used in 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 SUT performance spectrum. -Stable results are closer to the noiseless end of SUT preformance spectrum, -so MLRsearch may need to allow some frequency of high-loss trials -to ignore the reare but big effects near the noisefull end. - -MLRsearch is able to do such trial result filtering, but it needs -a configuration option to tell it how much frequent can the infrequent big loss be. -This option is called exceed ratio. It tells MLRsearch what ratio of trials -(more exactly what ratio of trial seconds) can have trial loss ratio -larger than goal loss ratio and still be classified as a lower bound. -Zero exceed ratio means all trials have to have 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 controlls 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, -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] prescibes wait intervals around trial traffic, -the MLRsearch algorithm may sum durations that are different -from the actual trial traffic durations. - -## Short Trials - -Section 24 of [RFC2544] already anticipates possible time savings -when short trials (shorter than full length trials) are used. - -MLRsearch requires each goal to specify its final trial duration. -Full-length trial is the short name for a trial whose intended trial duration -is equal to the goal final trial duration. - -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. - -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 intended load as input parameter -for a trial measurement, any load search algorithm needs to deal -with intended load values internally. - -But in presence of goals with non-zero loss ratio, the intended load -usually does not match the user 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, -especially with non-zero goal exceed 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. -More detailed explanations are given in the next chapter. - -Conditional throughput is partially related to load classification. -If a load is classified as a lower bound for a goal, -the conditional throughpt can be calculated, -and guaranteed to show effective loss ratio no larger than 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 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. - -Note that comparing 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 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 became 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 - -The main motivation for MLRsearch was to have an algorithm that spends less time -finding a throughput, either the [RFC2544] compliant one, -or some generalization thereof. The art of achieving short search times -is mainly in 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 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. -An implementation chosing loads very close to the current relevant lower bound -are quite likely to eventually stumble upon a trial result -with poor performance (due to SUT noise). -For an implementation chosing load 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. -The reason why it is unlikely to have two MLRsearch implementation showing -this kind of preference in load selection is precisely -in the desire to have short searches. -Even splits are the best way to achive the desired precision, -so the more optimized a search algorithm is for the overall search duration, -the better the repeatability and comparability -of its results will be, assuming the user configuration remains the same. - -Therefore, this document remains quite vague on load selection -and other optimisation details, and configuration attributes related to them. -Assuming users prefer libreries 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 -best repeatability at given search duration at different goals attribute values, -especially with respect to optional goal attributes -such as 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. - -Presence of other search goals does not affect 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. - -# Selected Functional Details - -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 can look into the next chapter -for more concise definitions. - -The two areas of focus in this chapter are the load classification -and the conditional throughput, starting with the latter. - -## Performance Spectrum - -There are several equivalent ways to define 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, and a finite set of trial results, all trials -measured at that load 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 have 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 sum of 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, and bout other quantiles -such as 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 load in question. -The sum of durations of those trials may be less than 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 is used. - -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 througput -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 duration one second and zero loss. -An imaginary trial is added with duration one second and zero forwarding rate. -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. - -The classification does not need the whole performance spectrum, -only few duration sums. - -A trial is called bad (according to a goal) if its trial loss ratio -is larger than the goal loss ratio. Trial that is not bad is called good. - -## Single Trial Duration - -When goal attributes are chosen in such a way that every trial has the same -intended duration, the load classification is sipler. - -The following description looks technical, but it follows the motivation -of goal loss ratio, goal exceed ratio and goal duration sum. -If sum of durations of all trials (at 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 is as many subsequent trials as needed -to make the sum of all trials to become 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 integer number of full-length 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. -Sum of durations of all bad full-length trials is called the bad sum. -Sum of durations of all good full-length trials is called the good sum. -The result of adding 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. -Optimistic exceed ratio is the bad sum divided by the whole sum. -Pessimistic exceed ratio is the bad sum plus the missing sum, that divided by -the whole sum. -If optimistic exceed ratio is larger than the goal exceed ratio, -the load is classified as an upper bound. -If 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 matches the logic in -the conditional throughput computation, so 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 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. - -Scenario one. The user had their reason for not configuring shorter goal -final trial duration. Perhaps SUT has buffers that may get full at longer -trial durations. Perhaps SUT shows periodic decreases of performance -the user does not want to treat as noise. In any case, many good short trials -may became bad full-length trial in the counter-factual case. -In extreme case, there are 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. - -Scenario two. 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 case, 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 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. - -Scenario three. Some SUTs are quite indifferent to trial duration. -Performance probability function constructed from short trial results -is likely to be similar to performance probability function constructed -from full-length trial results (perhaps with smaller dispersion, -but overall without big impact on the median quantiles). -For moderate goal exceed ratio values, 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 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 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 thas said, the logic differs from the single trial duration case -only in different definition of bad sum. -Good sum is still the sum across all good full-length trials. - -Few more notions are needed for definig the new bad sum. -Sum of durations of all bad full-length trials is called the bad long sum. -Sum of durations of all bad short trials is called the bad short sum. -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. -Else, 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 relevant bounds be -if only full-length trials were used for the search. - -Scenario one. If duration is too short, we expect to see higher frequency -of good short trials. This could lead to 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, -proably making it worse. - -Scenario two. Settings with small exceed ratio have small exceed coefficient, -so the impact of 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 impact as full-length bad trials. -The same conclusion applies for 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). - -Scenario three. Here excess sum is small in absolute value, as balancing sum -is expected to be 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 shorter search time -before selecting this load for measurement, bringing time savings -(while not affecting comparability). - -## Longer Trial Durations - -If there are trial results with intended duration larger -than the goal trial duration, the classification logic is intentionally undefined. - -The implementations MAY treat such longer trials as if they were full-length. -In any case, presence of such longer trials in either the relevant lower bound -or the relevant upper bound SHOULD be mentioned, as for sume SUTs -it is likely to affect comparability. - - -TODO: Here will be a chapter summarizing how MLRsearch library -adresses the problems from the Identified Problems chapter. - -{::comment} - - # Problems after MLRsearch - - 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 part of the MLRsearch specification, - so that future implementations may keep shortening the search duration even more. - - TODO: The rest is about attributes. - - 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 either side, - 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 - - Practice shows big improvements when multiple trials - and moderate exceed ratios are used. Mainly when it comes to result - repeatability, as sometimes it is not easy to distinguish - SUT noise from DUT instability. - - Conditional throughput has intuitive meaning when described - using the performance spectrum, so this is an improvement, - especially when compared to search procedures which use non-zero - goal loss ratio but return only the intended load at a lower bound. - - 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 us SUT noise - and what part is DUT performance fluctuations. - In practice, both types of noise tend to be too complicated for that analysis. - MLRsearch does not offer additional tools in this regard, - apart of giving users ability to search for more goals, - hoping to get more insight at the cost of longer overall search time. - - ## Repeatability and Comparability - - Multiple trials improve repeatability, depending on exceed ratio. - - In practice, 1s goal final trial duration with exceed ratio 0.5 - is good enough for modern SUTs (but that usually requires - smaller wait times around the traffic part of the trial, - otherwise too much search time is wasted 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. - TODO: Stress single value is for comparability, which one is due explainability. - - It is possible that the conditional throughput values (with non-zero - goal loss ratio) are better for repeatability than the relevant - lower bound values, especially for implementations - which pick load from a small set of discrete values. - - 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 - - Suported by the goal loss ratio attribute. - Improves repeatability as expected. - - ## 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 very best (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 - (and optional goal result attributes) - to become less conservative, but currently it is not clear - what impact would that have on repeatability. - -{:/comment} - -# IANA Considerations - -No requests of IANA. - -# Security Considerations - -Benchmarking activities as described in this memo are limited to -technology characterization of a DUT/SUT using controlled stimuli in a -laboratory environment, with dedicated address space and the constraints -specified in the sections above. - -The benchmarking network topology will be an independent test setup and -MUST NOT be connected to devices that may forward the test traffic into -a production network or misroute traffic to the test management network. - -Further, benchmarking is performed on a "black-box" basis, relying -solely on measurements observable external to the DUT/SUT. - -Special capabilities SHOULD NOT exist in the DUT/SUT specifically for -benchmarking purposes. Any implications for network security arising -from the DUT/SUT SHOULD be identical in the lab and in production -networks. - -# Acknowledgements - -Many thanks to Alec Hothan of OPNFV NFVbench project for thorough -review and numerous useful comments and suggestions. - -Special wholehearted gratitude and thanks to 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 encouragements full of empathy and -positive attitude. -Al, you are dearly missed. - -# Appendix A - -This is a specification of load classification. - -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 goal in question. -If both values are false, the load is classified as an upper bound. -Otherwise, the load is classifies as undecided. - -The pseudocole expects the following variables hold values as follows: - -- goal_duration_sum: The goal duration sum value. - -- goal_exceed_ratio: The goal exceed ratio value. - -- good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with 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 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 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 trial loss ratio - higher than the goal loss ratio. - -Here the implicit set of available trial results consists of all trials -measured at given intended load at the end of search. - -The code works correctly also when there are no trial results at given load. - -``` -balancing_sum = good_short_sum * goal_exceed_ratio / (1.0 - goal_exceed_ratio) -effective_bad_sum = bad_long_sum + max(0.0, bad_short_sum - balancing_sum) -effective_whole_sum = max(good_long_sum + effective_bad_sum, goal_duration_sum) -quantile_duration_sum = effective_whole_sum * goal_exceed_ratio -optimistic = effective_bad_sum <= quantile_duration_sum -pessimistic = (effective_whole_sum - good_long_sum) <= quantile_duration_sum -``` - -# Appendix B - -This is a specification of conditional throughput. - -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 pseudocole expects the following variables hold values as follows: - -- goal_duration_sum: The goal duration sum value. - -- goal_exceed_ratio: The goal exceed ratio value. - -- good_long_sum: Sum of durations across trials with trial duration - at least equal to the goal final trial duration and with 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 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 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. - -Here the implicit set of available trial results consists of all trials -measured at given intended load at the end of search. - -The code works correctly only when there if there is at leas one -trial result measured at given load. - -``` -all_long_sum = max(goal_duration_sum, good_long_sum + bad_long_sum) -remaining = all_long_sum * (1.0 - goal_exceed_ratio) -quantile_loss_ratio = None -for trial in long_trials: - if quantile_loss_ratio is None or remaining > 0.0: - quantile_loss_ratio = trial.loss_ratio - remaining -= trial.duration - else: - break -else: - if remaining > 0.0: - quantile_loss_ratio = 1.0 - -conditional_throughput = intended_load * (1.0 - quantile_loss_ratio) -``` - ---- back |