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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 diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md new file mode 100644 index 0000000000..ff63224a9d --- /dev/null +++ b/docs/ietf/draft-ietf-bmwg-mlrsearch-08.md @@ -0,0 +1,3351 @@ +--- + +title: Multiple Loss Ratio Search +abbrev: MLRsearch +docname: draft-ietf-bmwg-mlrsearch-08 +date: 2024-10-21 + +ipr: trust200902 +area: ops +wg: Benchmarking Working Group +kw: Internet-Draft +cat: info + +coding: us-ascii +pi: # can use array (if all yes) or hash here + toc: yes + sortrefs: # defaults to yes + symrefs: yes + +author: + - + ins: M. Konstantynowicz + name: Maciek Konstantynowicz + org: Cisco Systems + email: mkonstan@cisco.com + - + ins: V. Polak + name: Vratko Polak + org: Cisco Systems + email: vrpolak@cisco.com + +normative: + RFC1242: + RFC2285: + RFC2544: + RFC8219: + RFC9004: + +informative: + TST009: + target: https://www.etsi.org/deliver/etsi_gs/NFV-TST/001_099/009/03.04.01_60/gs_NFV-TST009v030401p.pdf + title: "TST 009" + FDio-CSIT-MLRsearch: + target: https://csit.fd.io/cdocs/methodology/measurements/data_plane_throughput/mlr_search/ + title: "FD.io CSIT Test Methodology - MLRsearch" + date: 2023-10 + PyPI-MLRsearch: + target: https://pypi.org/project/MLRsearch/1.2.1/ + title: "MLRsearch 1.2.1, Python Package Index" + date: 2023-10 + +--- abstract + +This document proposes extensions to [RFC2544] throughput search by +defining a new methodology called Multiple Loss Ratio search +(MLRsearch). MLRsearch aims to minimize search duration, +support multiple loss ratio searches, +and enhance result repeatability and comparability. + +The primary reason for extending [RFC2544] is to address the challenges +and requirements presented by the evaluation and testing +the data planes of software-based networking systems. + +To give users more freedom, MLRsearch provides additional configuration options +such as allowing multiple short trials per load instead of one large trial, +tolerating a certain percentage of trial results with higher loss, +and supporting the search for multiple goals with varying loss ratios. + +--- middle + +{::comment} + + As we use Kramdown to convert from Markdown, + we use this way of marking comments not to be visible in the rendered draft. + https://stackoverflow.com/a/42323390 + If another engine is used, convert to this way: + https://stackoverflow.com/a/20885980 + +[toc] + +{:/comment} + +# Purpose and Scope + +The purpose of this document is to describe the Multiple Loss Ratio search +(MLRsearch) methodology, optimized for determining +data plane throughput in software-based networking devices and functions. + +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 these problems, +the MLRsearch test methodology specification employs the following enhancements: + +- Allow multiple short trials instead of one big trial per load. + - Optionally, tolerate a percentage of trial results with higher loss. +- Allow searching for multiple Search Goals, with differing loss ratios. + - Any trial result can affect each Search Goal in principle. +- Insert multiple coarse targets for each Search Goal, earlier ones need + to spend less time on trials. + - Earlier targets also aim for lesser precision. + - Use Forwarding Rate (FR) at maximum offered load + [RFC2285] (Section 3.6.2) to initialize bounds. +- Take care when dealing with inconsistent trial results. + - Reported throughput is smaller than the smallest load with high loss. + - Smaller load candidates are measured first. +- Apply several load selection heuristics to save even more time + by trying hard to avoid unnecessarily narrow bounds. + +Some of these enhancements are formalized as MLRsearch specification, +the remaining enhancements are treated as implementation details, +thus achieving high comparability without limiting future improvements. + +MLRsearch configuration options are flexible enough to +support both conservative settings and aggressive settings. +The conservative settings lead to results +unconditionally compliant with [RFC2544], +but longer search duration and worse repeatability. +Conversely, aggressive settings lead to shorter search duration +and better repeatability, but the results are not compliant with [RFC2544]. + +No part of [RFC2544] is intended to be obsoleted by this document. + +# Identified Problems + +This chapter describes the problems affecting usability +of various performance testing methodologies, +mainly a binary search for [RFC2544] unconditionally compliant throughput. + +## Long Search Duration + +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 frame forwarding device to which stimulus is offered and + response measured [RFC2285] (Section 3.1.1). + +SUT as: + +- The collective set of network devices as a single entity to which + stimulus is offered and response measured [RFC2285] (Section 3.1.2). + +[RFC2544] specifies a test setup with an external tester stimulating the +networking system, treating it either as a single DUT, or as a system +of devices, an SUT. + +In the case of software networking, the SUT consists of not only the DUT +as a software program processing frames, but also of +server hardware and operating system functions, +with that server hardware resources shared across all programs including +the operating system. + +Given that the SUT is a shared multi-tenant environment +encompassing the DUT and other components, the DUT might inadvertently +experience interference from the operating system +or other software operating on the same server. + +Some of this interference can be mitigated. +For instance, +pinning DUT program threads to specific CPU cores +and isolating those cores can prevent context switching. + +Despite taking all feasible precautions, some adverse effects may still impact +the DUT's network performance. +In this document, these effects are collectively +referred to as SUT noise, even if the effects are not as unpredictable +as what other engineering disciplines call noise. + +DUT can also exhibit fluctuating performance itself, for reasons +not related to the rest of SUT. For example due to pauses in execution +as needed for internal stateful processing. +In many cases this +may be an expected per-design behavior, as it would be observable even +in a hypothetical scenario where all sources of SUT noise are eliminated. +Such behavior affects trial results in a way similar to SUT noise. +As the two phenomenons are hard to distinguish, +in this document the term 'noise' is used to encompass +both the internal performance fluctuations of the DUT +and the genuine noise of the SUT. + +A simple model of SUT performance consists of an idealized noiseless performance, +and additional noise effects. +For a specific SUT, the noiseless performance is assumed to be constant, +with all observed performance variations being attributed to noise. +The impact of the noise can vary in time, sometimes wildly, +even within a single trial. +The noise can sometimes be negligible, but frequently +it lowers the observed SUT performance as observed in trial results. + +In this model, SUT does not have a single performance value, it has a spectrum. +One end of the spectrum is the idealized noiseless performance value, +the other end can be called a noiseful performance. +In practice, trial result +close to the noiseful end of the spectrum happens only rarely. +The worse the performance value is, the more rarely it is seen in a trial. +Therefore, the extreme noiseful end of the SUT spectrum is not observable +among trial results. +Also, the extreme noiseless end of the SUT spectrum +is unlikely to be observable, this time because some small noise effects +are likely to occur multiple times during a trial. + +Unless specified otherwise, this document's focus is +on the potentially observable ends of the SUT performance spectrum, +as opposed to the extreme ones. + +When focusing on the DUT, the benchmarking effort should ideally aim +to eliminate only the SUT noise from SUT measurements. +However, +this is currently not feasible in practice, as there are no realistic enough +models available to distinguish SUT noise from DUT fluctuations, +based on authors' experience and available literature. + +Assuming a well-constructed SUT, the DUT is likely its +primary performance bottleneck. +In this case, we can define the DUT's +ideal noiseless performance as the noiseless end of the SUT performance spectrum, +especially for throughput. +However, other performance metrics, such as latency, +may require additional considerations. + +Note that by this definition, DUT noiseless performance +also minimizes the impact of DUT fluctuations, as much as realistically possible +for a given trial duration. + +MLRsearch methodology aims to solve the DUT in SUT problem +by estimating the noiseless end of the SUT performance spectrum +using a limited number of trial results. + +Any improvements to the throughput search algorithm, aimed at better +dealing with software networking SUT and DUT setup, should employ +strategies recognizing the presence of SUT noise, allowing the discovery of +(proxies for) DUT noiseless performance +at different levels of sensitivity to SUT noise. + +## Repeatability and Comparability + +[RFC2544] does not suggest to repeat throughput search. +And from just one +discovered throughput value, it cannot be determined how repeatable that value is. +Poor repeatability then leads to poor comparability, +as different benchmarking teams may obtain varying throughput values +for the same SUT, exceeding the expected differences from search precision. + +[RFC2544] throughput requirements (60 seconds trial and +no tolerance of a single frame loss) affect the throughput results +in the following way. +The SUT behavior close to the noiseful end of its performance spectrum +consists of rare occasions of significantly low performance, +but the long trial duration makes those occasions not so rare on the trial level. +Therefore, the binary search results tend to wander away from the noiseless end +of SUT performance spectrum, more frequently and more widely than short +trials would, thus causing poor throughput repeatability. + +The repeatability problem can be addressed by defining a search procedure +that identifies a consistent level of performance, +even if it does not meet the strict definition of throughput in [RFC2544]. + +According to the SUT performance spectrum model, better repeatability +will be at the noiseless end of the spectrum. +Therefore, solutions to the DUT in SUT problem +will help also with the repeatability problem. + +Conversely, any alteration to [RFC2544] throughput search +that improves repeatability should be considered +as less dependent on the SUT noise. + +An alternative option is to simply run a search multiple times, and report some +statistics (e.g. average and standard deviation). +This can be used +for a subset of tests deemed more important, +but it makes the search duration problem even more pronounced. + +## Throughput with Non-Zero Loss + +[RFC1242] (Section 3.17) 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 load that satisfies a non-zero Goal Loss Ratio. +But it is not that obvious how to search for multiple goals at once, +hence the support for multiple Search Goals remains a problem. + +## Inconsistent Trial Results + +While performing throughput search by executing a sequence of +measurement trials, there is a risk of encountering inconsistencies +between trial results. + +The plain bisection never encounters inconsistent trials. +But [RFC2544] hints about the possibility of inconsistent trial results, +in two places in its text. +The first place is section 24, where full trial durations are required, +presumably because they can be inconsistent with the results +from short trial durations. +The second place is section 26.3, where two successive zero-loss trials +are recommended, presumably because after one zero-loss trial +there can be a subsequent inconsistent non-zero-loss trial. + +Examples include: + +- A trial at the same load (same or different trial duration) results + in a different Trial Loss Ratio. +- A trial at a higher load (same or different trial duration) results + in a smaller Trial Loss Ratio. + +Any robust throughput search algorithm needs to decide how to continue +the search in the presence of such inconsistencies. +Definitions of throughput in [RFC1242] and [RFC2544] are not specific enough +to imply a unique way of handling such inconsistencies. + +Ideally, there will be a definition of a new quantity which both generalizes +throughput for non-zero Goal Loss Ratio values +(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. + +Relevant Lower Bound is the MLRsearch term that addresses this problem. + +# MLRsearch Specification + +MLRsearch specification describes all technical +definitions needed for evaluating whether a particular test procedure +complies with MLRsearch specification. + +{::comment} + [Good idea for 08, maybe ask BMWG first?] + + <mark>TODO VP: Separate Requirements and Recommendations/Suggestions + paragraphs? (currently requirements are in discussion subsections - + discussion should only clarify things without adding new + requirements)</mark> +{:/comment} + +Some terms used in the specification are capitalized. +It is just a stylistic choice for this document, +reminding the reader this term is introduced, defined or explained +elsewhere in the document. +Lowercase variants are equally valid. + +Each per term subsection contains a short **Definition** paragraph +containing a minimal definition and all strict REQUIREMENTS, followed +by **Discussion** paragraphs containing some important consequences and +RECOMMENDATIONS. +Other text in this section discusses document structure +and non-authoritative summaries. + +## Overview + +MLRsearch Specification describes a set of abstract system components, +acting as functions with specified inputs and outputs. + +A test procedure is said to comply with MLRsearch Specification +if it can be conceptually divided into analogous components, +each satisfying requirements for the corresponding MLRsearch component. +Any such compliant test procedure is called a MLRsearch Implementation. + +The Measurer component is tasked to perform Trials, +the Controller component is tasked to select Trial Durations and Loads, +the Manager component is tasked to pre-configure everything +and to produce the test report. +The test report explicitly states Search Goals (as Controller inputs) +and corresponding Goal Results (Controller outputs). + +The Manager calls the Controller once, +the Controller keeps calling the Measurer +until all stopping conditions are met. + +The part where Controller calls the Measurer is called the Search. +Any activity done by the Manager before it calls the Controller +(or after Controller returns) is not considered to be part of the Search. + +MLRsearch Specification prescribes regular search results and recommends +their stopping conditions. Irregular search results are also allowed, +they may have different requirements and stopping conditions. + +Search results are based on Load Classification. +When measured enough, any chosen Load can either achieve or fail +each Search Goal (separately), thus becoming +a Lower Bound or an Upper Bound for that Search Goal. + +When the Relevant Lower Bound is close enough to Relevant Upper Bound +according to Goal Width, the Regular Goal Result is found. +Search stops when all Regular Goal Results are found, +or when some Search Goals are proven to have only Irregular Goal Results. + +{::comment} + + TODO-P1: An implementation may add additional attributes to inputs and outputs. + + TODO-P1: An implementation may require some attributes not required by specification. + + TODO-P1: An implementation may support "missing" attributes by applying "reasonable defaults". + +{:/comment} + +## Quantities + +MLRsearch specification uses a number of specific quantities, +some of them can be expressed in several different units. + +In general, MLRsearch specification does not require particular units to be used, +but it is REQUIRED for the test report to state all the units. +For example, ratio quantities can be dimensionless numbers between zero and one, +but may be expressed as percentages instead. + +For convenience, a group of quantities can be treated as a composite quantity, +One constituent of a composite quantity is called an attribute, +and a group of attribute values is called an instance of that composite quantity. + +Some attributes are not independent from others, +and they can be calculated from other attributes. +Such quantites are called derived quantities. + +## Existing Terms + +{::comment} + + TODO-P1: Merge into Glossary! MK - IMV this section should stay here as is. + +{:/comment} + +This specification relies on the following three documents that should +be consulted before attempting to make use of this document: + +- RFC 1242 "Benchmarking Terminology for Network Interconnect Devices" + contains basic term definitions. + +- RFC 2285 "Benchmarking Terminology for LAN Switching Devices" adds + more terms and discussions, describing some known network + benchmarking situations in a more precise way. + +- RFC 2544 "Benchmarking Methodology for Network Interconnect Devices" + contains discussions of a number of terms and additional methodology + requirements. + +Definitions of some central terms from above documents are copied and +discussed in the following subsections. + +{::comment} + [Good idea for 08, but needs more work. Ask BMWG?] + + Alternatively, quick list of all (existing and new here) terms, + with links (external or internal respectively) to definitions. + + <mark>MKP3 [VP] TODO: Even if the following list will not be in final draft, + it is useful to keep it around (maybe commented-out) while editing.</mark> + + <mark>MKP3 VP note: rough list of all RFC references: + - [RFC1242] (section 3.17 Throughput) ... definition + - [RFC2544] (section 26.1 Throughput) ... methodology + - [RFC2544] (section 24. Trial duration): + - full trial durations (implies short trials) + - Also 60s for unconditional compliance is here. + - Also "the search" (without quotes) appears there. + - Also "binary search" (with quotes) appears there. + - [RFC2544] (section 26.3 Frame loss rate): + - two successive zero-loss trials are recommended (hints about loss inversion) + - un/conditionally compliant with [RFC2544] + - [RFC2544] (section 26. Benchmarking tests:) + - all its "dot sections" have "Reporting format:" paragraphs + - (implies test report) + - [RFC2544] (section 26.1 Throughput) wants graph, frame size on X axis. + - [RFC2544] (section 23. Trial description) trial + - general description of trial + - wait times specifically, maybe also learning frames? + - Data Rate of [RFC2544] (section 14. Bidirectional traffic) + - seems equal to input frame rate [RFC2544] (23. Trial description). + - [RFC2544] (section 21. Bursty traffic) suggests non-constant loads? + - Intended Load of [RFC2285] (section 3.5.1 Intended load (Iload)) + - [RFC2285] (Section 3.5.2 Offered load (Oload)) + - Forwarding Rate as defined in [RFC2285] (section 3.6.1 Forwarding rate (FR)) + - [RFC2285] (3.5.3 Maximum offered load (MOL)) + - reordered frames [RFC2544] (section 10. Verifying received frames) + - For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, + as recommended from [RFC2544] (section 8. Frame formats) + and [RFC2544] (section 12. Protocol addresses). + - [RFC8219] (section 5.3. Traffic Setup) introduces traffic setups consisting of a mix of IPv4 and IPv6 traffic + - [RFC2544] (section 9. Frame sizes) + - [RFC1242] (section 3.5 Data link frame size) + - [RFC2285] (section 3.6.2) FRMOL + - [RFC2285] (section 3.1.1) DUT + - [RFC2285] (section 3.1.2) SUT + - [RFC2544] (section 6. Test set up) test setup with (an external) tester + - [RFC9004] B2B + - [RFC8219] (section 5.3. Traffic Setup) for an example of ip4+ip6 mixed traffic + </mark> + +{:/comment} + +{::comment} + [Important, just not enough time in 07.] + + <mark>MKP3 [VP] TODO: Verify that MLRsearch specification does not discuss + meaning of existing terms without quoting their original definition.</mark> + +{:/comment} + +### SUT + +Defined in [RFC2285] (Section 3.1.2) as follows. + +Definition: + +The collective set of network devices to which stimulus is offered +as a single entity and response measured. + +Discussion: + +An SUT consisting of a single network device is also allowed. + +### DUT + +Defined in [RFC2285] (Section 3.1.1) as follows. + +Definition: + +The network forwarding device to which stimulus is offered and +response measured. + +Discussion: + +DUT, as a sub-component of SUT, is only indirectly mentioned +in MLRsearch specification, but is of key relevance for its motivation. + +### Trial + +A trial is the part of the test described in [RFC2544] (Section 23). + +Definition: + + A particular test consists of multiple trials. Each trial returns + one piece of information, for example the loss rate at a particular + input frame rate. Each trial consists of a number of phases: + + a) If the DUT is a router, send the routing update to the "input" + port and pause two seconds to be sure that the routing has settled. + + b) Send the "learning frames" to the "output" port and wait 2 + seconds to be sure that the learning has settled. Bridge learning + frames are frames with source addresses that are the same as the + destination addresses used by the test frames. Learning frames for + other protocols are used to prime the address resolution tables in + the DUT. The formats of the learning frame that should be used are + shown in the Test Frame Formats document. + + c) Run the test trial. + + d) Wait for two seconds for any residual frames to be received. + + e) Wait for at least five seconds for the DUT to restabilize. + +Discussion: + +The definition describes some traits, and it is not clear whether all of them +are REQUIRED, or some of them are only RECOMMENDED. + +Trials are the only stimuli the SUT is expected to experience +during the Search. + +For the purposes of the MLRsearch specification, +it is ALLOWED for the test procedure to deviate from the [RFC2544] description, +but any such deviation MUST be described explicitly in the test report. + +In some discussion paragraphs, it is useful to consider the traffic +as sent and received by a tester, as implicitly defined +in [RFC2544] (Section 6). + +{::comment} + + TODO-P2: Assert traffic is sent only in phase c) and received in phases c) and d). + +{:/comment} + +An example of deviation from [RFC2544] is using shorter wait times, +compared to those described in phases b), d) and e). + +## Trial Terms + +This section defines new and redefine existing terms for quantities +relevant as inputs or outputs of a Trial, as used by the Measurer component. + +### Trial Duration + +Definition: + +Trial Duration is the intended duration of the traffic part of a Trial. + +Discussion: + +This quantity does not include any preparation nor waiting +described in section 23 of [RFC2544] (Section 23). + +While any positive real value may be provided, some Measurer implementations +MAY limit possible values, e.g. by rounding down to nearest integer in seconds. +In that case, it is RECOMMENDED to give such inputs to the Controller +so the Controller only proposes the accepted values. + +### Trial Load + +Definition: + +Trial Load is the per-interface Intended Load for a Trial. + +Discussion: + +For test report purposes, it is assumed that this is a constant load by default, +as specified in [RFC1242] (Section 3.4). + +Trial Load MAY be only an average load, +e.g. when the traffic is intended to be bursty, +e.g. as suggested in [RFC2544] (Section 21). +In the case of non-constant load, the test report +MUST explicitly mention how exactly non-constant the traffic is. + +Trial Load is equivalent to the quantities defined +as constant load of [RFC1242] (Section 3.4), +data rate of [RFC2544] (Section 14), +and Intended Load of [RFC2285] (Section 3.5.1), +in the sense that all three definitions specify that this value +applies to one (input or output) interface. + +For test report purposes, multi-interface aggregate load MAY be reported, +and is understood as the same quantity expressed using different units. +From the report it MUST be clear whether a particular Trial Load value +is per one interface, or an aggregate over all interfaces. + +Similarly to Trial Duration, some Measurers may limit the possible values +of trial load. Contrary to trial duration, the test report is NOT REQUIRED +to document such behavior, as in practice the load differences +are negligible (and frequently undocumented). + +It is ALLOWED to combine Trial Load and Trial Duration values in a way +that would not be possible to achieve using any integer number of data frames. + +If a particular Trial Load value is not tied to a single Trial, +e.g. if there are no Trials yet or if there are multiple Trials, +this document uses a shorthand **Load**. + +{::comment} + [I feel this is important, to be discussed separately (not in-scope).] + + <mark>MKP2 [VP] TODO: Explain why are we not using Oload. + 1. MLRsearch implementations cannot react correctly to big differences + between Iload and Oload. + 2. The media between the tested and the DUT are thus considered to be part of SUT. + If DUT causes congestion control, it is not expected to handle Iload. + </mark> + + See further discussion in [Trial Forwarding Ratio](#trial-forwarding-ratio) + and in [Measurer ](#measurer) sections for other related issues. + + <mark>MKP2 [VP] TODO: Create a separate subsection for Oload discussion, + or clearly separate which aspects are discussed under which term.</mark> + + <mark>MKP2 [VP] TODO: New idea. Compare the tester to an ordinary router + in some datacenter. The Intended Load is not jst some abstract input. + It is the real traffic coming from routers next hop farther. + It does not matter that DUT has forwarded each frame it received, + if the tester was unable to sent all the traffic in time. + Endpoint see packet loss, they do not care about [RFC2285] + half-duplex, spanning trees, nor congestion control mechanisms. + Formally speaking, I consider even the sending interface of the sender + to be the part of SUT. + Reading [RFC2285] (section 3.5.3 Maximum offered load (MOL)) + "This will be the case when an external source lacks the resources + to transmit frames at the minimum legal inter-frame gap" + that means TRex workers are also part of SUT. If they do not have + enough CPU power to generate frames are required, those frames are lost. + </mark> + + <mark>MKP2 [VP] TODO: That new idea warants some discussion in "DUT within SUT", + as it is just another case of ther rest of SUT ruining + otherwise good DUT performance.</mark> + +{:/comment} + +### Trial Input + +Definition: + +Trial Input is a composite quantity, consisting of two attributes: +Trial Duration and Trial Load. + +Discussion: + +When talking about multiple Trials, it is common to say "Trial Inputs" +to denote all corresponding Trial Input instances. + +A Trial Input instance acts as the input for one call of the Measurer component. + +Contrary to other composite quantities, MLRsearch implementations +are NOT ALLOWED to add optional attributes here. +This improves interoperability between various implementations of +the Controller and the Measurer. + +### Traffic Profile + +Definition: + +Traffic Profile is a composite quantity containing +all attributes other than Trial Load and Trial Duration, +that are needed for unique determination of the trial to be performed. + +Discussion: + +All the attributes are assumed to be constant during the search, +and the composite is configured on the Measurer by the Manager +before the search starts. +This is why the traffic profile is not part of the Trial Input. + +As a consequence, implementations of the Manager and the Measurer +must be aware of their common set of capabilities, so that Traffic Profile +instance uniquely defines the traffic during the Search. +The important fact is that none of those capabilities +have to be known by the Controller implementations. + +The Traffic Profile SHOULD contain some specific quantities defined elsewhere. +For example [RFC2544] (Section 9) governs +data link frame sizes as defined in [RFC1242] (Section 3.5). + +Several more specific quantities may be RECOMMENDED, depending on media type. +For example, [RFC2544] (Appendix C) lists frame formats and protocol addresses, +as recommended in [RFC2544] (Section 8) and [RFC2544] (Section 12). + +Depending on SUT configuration, e.g. when testing specific protocols, +additional attributes MUST be included in the traffic profile +and in the test report. + +Example: [RFC8219] (Section 5.3) introduces traffic setups +consisting of a mix of IPv4 and IPv6 traffic - the implied traffic profile +therefore must include an attribute for their percentage. + +Other traffic properties that need to be somehow specified in Traffic +Profile, if they apply to the test scenario, include: + +- bidirectional traffic from [RFC2544] (Section 14), + +- fully meshed traffic from [RFC2285] (Section 3.3.3), + +- and modifiers from [RFC2544] (Section 11). + +### Trial Forwarding Ratio + +Definition: + +The Trial Forwarding Ratio is a dimensionless floating point value. +It MUST range between 0.0 and 1.0, both inclusive. +It is calculated by dividing the number of frames +successfully forwarded by the SUT +by the total number of frames expected to be forwarded during the trial. + +Discussion: + +For most Traffic Profiles, "expected to be forwarded" means +"intended to get transmitted from Tester towards SUT". +Only if this is not the case, the test report MUST describe the Traffic Profile +in a way that implies how Trial Forwarding Ratio should be calculated. + +Trial Forwarding Ratio MAY be expressed in other units +(e.g. as a percentage) in the test report. + +Note that, contrary to loads, frame counts used to compute +trial forwarding ratio are aggregates over all SUT output interfaces. + +Questions around what is the correct number of frames +that should have been forwarded +is generally outside of the scope of this document. + +{::comment} + + TODO-P0: Mention iload/oload difference is also out of scope. + + TODO-P2: Mention duplicate, previous-trial and other "more than + expected" frame counts are out of scope. Recommend to count them as + loss? MK there should be a reference about the last TODO in 1242 2285 + or 2544. + +{:/comment} + +{::comment} + [Part two of iload/oload discussion.] + + See discussion in [Measurer ](#measurer) section + for more details about calibrating test equipment. + + <mark>MKP2 [VP] TODO: Define unsent frames?</mark> + + <mark>MKP2 [VP] TODO: If Oload is fairly below Iload, the unsent frames + should be counted as lost, otherwise search outputs are misleading. + But what is "fairly"? CSIT tolerates 10 microseconds worth of unsent frames.</mark> + +{:/comment} + +{::comment} + [Low priority, but maybe useful for somebody?] + + <mark>MKP2 [VP] TODO: Mention traffic profiles with uneven frame counts? + E.g. when SUT is expected to perform IP packet fragmentation or reassembly. + </mark> + +{:/comment} + +### Trial Loss Ratio + +Definition: + +The Trial Loss Ratio is equal to one minus the Trial Forwarding Ratio. + +Discussion: + +100% minus the Trial Forwarding Ratio, when expressed as a percentage. + +This is almost identical to Frame Loss Rate of [RFC1242] (Section 3.6). +Te only minor differences are that Trial Loss Ratio +does not need to be expressed as a percentage, +and Trial Loss Ratio is explicitly based on aggregate frame counts. + +### Trial Forwarding Rate + +Definition: + +The Trial Forwarding Rate is a derived quantity, calculated by +multiplying the Trial Load by the Trial Forwarding Ratio. + +Discussion: + +It is important to note that while similar, this quantity is not identical +to the Forwarding Rate as defined in [RFC2285] (Section 3.6.1). +The latter is specific to one output interface only, +whereas the Trial Forwarding Ratio is based +on frame counts aggregated over all SUT output interfaces. + +In consequence, for symmetric traffic profiles the Trial Forwarding Rate value +is equal to arithmetric average of [RFC2285] Forwarding Rate values +across all active interfaces. + +{::comment} + [Part 3 of iload/oload discussion.] + + <mark>MKP2 [VP] TODO: If some unsent frames were tolerated (not counted as lost), + this value is actually higher than the real fps output of the SUT. + Should we use the real FR as the basis for Conditional Throughput + (instead of this TFR)? That would require additional Trial Output attribute. + </mark> + + <mark>MKP2 [VP] TODO: What about duration stretching? + This also causes difference between Iload and Oload, + but in an invisible way.</mark> + + <mark>MKP2 [VP] TODO: Recommend start+sleep+stop? + How long wait for late frames? RFC2544 2s is too much even at 30s trial.</mark> + +{:/comment} + +### Trial Effective Duration + +Definition: + +Trial Effective Duration is a time quantity related to the trial, +by default equal to the Trial Duration. + +Discussion: + +This is an optional feature. +If the Measurer does not return any Trial Effective Duration value, +the Controller MUST use the Trial Duration value instead. + +Trial Effective Duration may be any time quantity chosen by the Measurer +to be used for time-based decisions in the Controller. + +The test report MUST explain how the Measurer computes the returned +Trial Effective Duration values, if they are not always +equal to the Trial Duration. + +This feature can be beneficial for users +who wish to manage the overall search duration, +rather than solely the traffic portion of it. +Simply measure the duration of the whole trial (including all wait times) +and use that as the Trial Effective Duration. + +This is also a way for the Measurer to inform the Controller about +its surprising behavior, for example when rounding the Trial Duration value. + +{::comment} + [Not very important, but easy and nice recommendation.] + + <mark>MKP2 [VP] TODO: Recommend for Controller to return all trials at relevant bounds, + as that may better inform users when surprisingly small amount of trials + was performed, just because the the trial effective duration values were big.</mark> + + <mark>MKP2 [VP] TODO: Repeat that this is not here to deal with duration stretching.</mark> + +{:/comment} + +### Trial Output + +Definition: + +Trial Output is a composite quantity. The REQUIRED attributes are +Trial Loss Ratio, Trial Effective Duration and Trial Forwarding Rate. + +Discussion: + +When talking about multiple trials, it is common to say "Trial Outputs" +to denote all corresponding Trial Output instances. + +Implementations may provide additional (optional) attributes. +The Controller implementations MUST ignore values of any optional attribute +they are not familiar with, +except when passing Trial Output instances to the Manager. + +Example of an optional attribute: +The aggregate number of frames expected to be forwarded during the trial, +especially if it is not just (a rounded-down value) +implied by Trial Load and Trial Duration. + +While [RFC2285] (Section 3.5.2) requires the Offered Load value +to be reported for forwarding rate measurements, +it is NOT REQUIRED in MLRsearch Specification, +as search results do not depend on it. + +{::comment} + + TODO-P1: MK note - i know that Offered Load can be calculated from Trial + Loss Ratio and Trial Forwarding Rate but still most/all network users + would expect to know what Trial Load was used. Also, saying that search + results do not depend on Offered Load or Trial Load is not true :) + VP note - I partially disagree and partially do not understand. + +{:/comment} + +{::comment} + + [Side tangent from iload/oload discussion. Stilll recommendation is not obvious.] + + <mark>MKP2 mk edit note: we need to more explicitly address + the relevance or irrelevance of [RFC2285] (Section 3.5.2 Offered load (Oload)). + Current text in [Trial Load](#trial-load) is ambiguous - quoted below.</mark> + + <mark>MKP2 "Questions around what is the correct number of frames that should + have been forwarded is generally outside of the scope of this document. + See discussion in [Measurer ](#measurer) section for more details about + calibrating test equipment."</mark> + +{:/comment} + +### Trial Result + +Definition: + +Trial Result is a composite quantity, +consisting of the Trial Input and the Trial Output. + +Discussion: + +When talking about multiple trials, it is common to say "trial results" +to denote all corresponding Trial Result instances. + +While implementations SHOULD NOT include additional attributes +with independent values, they MAY include derived quantities. + +## Goal Terms + +This section defines new terms for quantities relevant (directly or indirectly) +for inputs or outputs of the Controller component. + +Several goal attributes are defined before introducing +the main composite quantity: the Search Goal. + +{::comment} + + TODO-P0: Mention definitions are not informative? + E.g. Goal Final Trial Duration and Goal Initial Trial Duration + have the same Definition text. + Note that these are already fixed for now, but other attributes need review. + +{:/comment} + +Discussions within this section are short, informal, +and referencing future sections, with the impact on search results +discussed only after introducing complete set of auxiliary terms. + +### Goal Final Trial Duration + +{::comment} + + TODO-P0: review updated definition, check if any informal explanation is needed. + +{:/comment} + +Definition: + +Minimum value for Trial Duration required for classifying the Load +as a Lower Bound. + +Discussion: + +This attribute value MUST be positive. + +Informally, while MLRsearch is allowed to perform trials shorter than this value, +the results from such short trials have only limited impact on search results. + +It is RECOMMENDED for all search goals to share the same +Goal Final Trial Duration value. +Otherwise, Trial Duration values larger than the Goal Final Trial Duration +may occur, weakening the assumptions +the [Load Classification Logic](#load-classification-logic) is based on. + +{::comment} + + TODO-P2: Currently not covered well in Logic chapter? + + TODO-P2: Maybe change fourth goal there to show this? + +{:/comment} + +### Goal Duration Sum + +Definition: + +A threshold value for a particular sum of Trial Effective Duration values. + +Discussion: + +This attribute value MUST be positive. + +Informally, this prescribes the maximum amount of trials performed +at a specific Trial Load and Goal Final Trial Duration during the search. + +If the Goal Duration Sum is larger than the Goal Final Trial Duration, +multiple trials may need to be performed at the same load. + +See [MLRsearch Compliant with TST009](#mlrsearch-compliant-with-tst009) +for an example where possibility of multiple trials at the same load is intended. + +A Goal Duration Sum value lower than the Goal Final Trial Duration +(of the same goal) could save some search time, but is NOT RECOMMENDED. + +{::comment} + + TODO-P2: Currently not covered in the classification logic chapter. + +{:/comment} + +### Goal Loss Ratio + +Definition: + +A threshold value for Trial Loss Ratio values. + +Discussion: + +Attribute value MUST be non-negative and smaller than one. + +A trial with Trial Loss Ratio larger than this value +signals the SUT may be unable to process this Trial Load well enough. + +See [Throughput with Non-Zero Loss](#throughput-with-non-zero-loss) +why users may want to set this value above zero. + +### Goal Exceed Ratio + +Definition: + +A threshold value for a particular ratio of sums of Trial Effective Duration +values. + +Discussion: + +Attribute value MUST be non-negative and smaller than one. + +Informally, up to this proportion of High-Loss Trials +(Trial Results with Trial Loss Ratio above Goal Loss Ratio) +is tolerated at a Lower Bound. + +For explainability reasons, the RECOMMENDED value for exceed ratio is 0.5 (50%), +as it simplifies some concepts by relating them to the concept of median. +Also, the value of 50% leads to smallest variation in overall Search Duration +in practice. + +See [Exceed Ratio and Multiple Trials](#exceed-ratio-and-multiple-trials) +section for more details. + +### Goal Width + +Definition: + +A threshold value for deciding whether two Trial Load values are close enough. + +Discussion: + +It is an optional attribute. If present, the value MUST be positive. + +Informally, this acts as a stopping condition, +controlling the precision of the search. +The search stops if every goal has reached its precision. + +Implementations without this attribute +MUST give the Controller other ways to control the search stopping conditions. + +Absolute load difference and relative load difference are two popular choices, +but implementations may choose a different way to specify width. + +The test report MUST make it clear what specific quantity is used as Goal Width. + +{::comment} + + TODO-P2: Comment: While not needed for precision purposes + larger-than-width result (e.g. when time is up) is still an Irregular result, + so this is the way to make sure it looks irregular in report. + +{:/comment} + +It is RECOMMENDED to set the Goal Width (as relative difference) value +to a value no smaller than the Goal Loss Ratio. +If the reason is not obvious, see the details in +[Generalized Throughput](#generalized-throughput). + +### Goal Initial Trial Duration + +{::comment} + + TODO-P0: review updated definition, check if any informal explanation is needed. + +{:/comment} + +Definition: + +Minimum value for Trial Duration required for classifying the Load as any Bound. + +Discussion: + +This is an example of an OPTIONAL Search Goal some implementations may support. + +The reasonable default value is equal to the Goal Final Trial Duration value. + +If present, this value MUST be positive. + +Informally, this is the smallest Trial Duration the Controller will select +when focusing on the goal. + +Strictly speaking, Trial Results with smaller Trial Duration values +are still accepted by the Load Classification logic. +This is just a way for the user to discourage trials with Trial Duration +values deemed as too unreliable for this SUT and this Search Goal. + +### Search Goal + +Definition: + +The Search Goal is a composite quantity consisting of several attributes, +some of them are required. + +Required attributes: +- Goal Final Trial Duration +- Goal Duration Sum +- Goal Loss Ratio +- Goal Exceed Ratio + +Optional attributes: +- Goal Initial Trial Duration +- Goal Width + +Discussion: + +Implementations MAY add their own attributes. +Those additional attributes may be required by the implementation +even if they are not required by MLRsearch specification. +But it is RECOMMENDED for those implementations +to support missing values by providing reasonable default values. + +{::comment} + + TODO2: MK last sentence doesn't make sense. + VP: Added TODOs to Overview section. + +{:/comment} + +See [Compliance ](#compliance) for important Search Goal instances. + +### Controller Input + +Definition: + +Controller Input is a composite quantity +required as an input for the Controller. +The only REQUIRED attribute is a list of Search Goal instances. + +Discussion: + +MLRsearch implementations MAY use additional attributes. +Those additional attributes may be required by the implementation +even if they are not required by MLRsearch specification. + +Formally, the Manager does not apply any Controller configuration +apart from one Controller Input instance. + +For example, Traffic Profile is configured on the Measurer by the Manager, +without explicit assistance of the Controller. + +{::comment} + + TODO-P0: This paragraph is for implementers. + + TODO2: MK implementation hints are fine, and do not have to be preceded +with any remark of the sort you're suggesting IMV. + +{:/comment} + +The order of Search Goal instances in a list SHOULD NOT +have a big impact on Controller Output, +but MLRsearch implementations MAY base their behavior on the order +of Search Goal instances in a list. + +{::comment} + [User recommendation, we should have separate section summarizing those.] + + Also, it is recommended to avoid "incomparable" goals, e.g. one with + lower loss ratio but higher exceed ratio, and other with higher loss ratio + but lower loss ratio. In worst case, this can make the search to last too long. + Implementations are RECOMMENDED to sort the goals and start with + stricter ones first, as bounds for those will not get invalidated + byt measureing for less trict goal later in the search. + +{:/comment} + +#### Max Load + +Definition: + +Max Load is an optional attribute of Controller Input. +It is the maximal value the Controller is allowed to use for Trial Load values. + +Discussion: + +Max Load is an example of an optional attribute (outside the list of Search Goals) +required by some implementations of MLRsearch. + +In theory, each search goal could have its own Max Load value, +but as all trials are possibly affecting all Search Goals, +it makes more sense for a single Max Load value to apply +to all Search Goal instances. + +While Max Load is a frequently used configuration parameter, already governed +(as maximum frame rate) by [RFC2544] (Section 20) +and (as maximum offered load) by [RFC2285] (Section 3.5.3), +some implementations may detect or discover it +(instead of requiring a user-supplied value). + +{::comment} + + TODO-P0: Move this (and goal width) to RUB discussion or other explanation instead. + + TODO2: MK i think it belongs here, as input parameter. may refer to + section "Hard Performance Limit" though. + +{:/comment} + +In MLRsearch specification, one reason for listing +the [Relevant Upper Bound](#relevant-upper-bound) as a required attribute +is that it makes the search result independent of Max Load value. + +{::comment} + + TODO2: MK RUB is not an attribute, it's Result Term. Hence above + sentence does not make sense and should be removed. + VP: RUB is an attribure of Goal Result composite quantity. + +{:/comment} + +{::comment} + [Not important directly, may matter for iload/oload.] + + <mark>MKP2 [VP] TODO: 2544 and 2285 care about half-duplex media. Should we?</mark> + +{:/comment} + +{::comment} + [Maybe obvious but I think useful. RFC2544 talks about header compression in WANs.] + + <mark>MKP2 [VP] TODO: Mention that Max Load should care about all media within SUT, + including DUT-DUT links. Important when that link carries encapsulated traffic, + as bandwidth limit there implies lower max rate + (than implied by tester-SUT links).</mark> + +{:/comment} + +#### Min Load + +Definition: + +Min Load is an optional attribute of Controller Input. +It is the minimal value the Controller is allowed to use for Trial Load values. + +Discussion: + +Min Load is another example of an optional attribute +required by some implementations of MLRsearch. +Similarly to Max Load, it makes more sense to prescribe one common value, +as opposed to using a different value for each Search Goal. + +Min Load is mainly useful for saving time by failing early, +arriving at an Irregular Goal Result when Min Load gets classified +as an Upper Bound. + +For implementations, it is useful to require Min Load to be non-zero +and large enough to result in at least one frame being forwarded +even at smallest allowed Trial Duration, +so Trial Loss Ratio is always well-defined, +and the implementation can use relative Goal Width +(without running into issues around zero Trial Load value). + +{::comment} + + TODO2: MK last 3 lines need to be reworded, as they don't make sense, + and i can't suggest alternative wording. + +{:/comment} + +## Auxiliary Terms + +While the terms defined in this section are not strictly needed +when formulating MLRsearch requirements, they simplify the language used +in discussion paragraphs and explanation chapters. + +### Current and Final Quantities + +{::comment} + + TODO2: MK doesn't this content belong to "Quantities" section at the + beginning of the doc? + VP: Probably yes, should be moved. + +{:/comment} + +Some quantites are defined in a way that allows them to be computed +in the middle of the Search. Other quantities are specified in a way +that allows them to be computed only after the Search ends. +And some quantities are important only after the Search ended, +but are computable also before the Search ends. + +The adjective **current** marks a quantity that is computable +before the Search ends, but the computed value may change during the Search. +When such value is relevant for the search result, the adjective **final** +may be used to denote the value at the end of the Search. + +{::comment} + + TODO2: MK **current** and **final** adjectives seem to relate to values + of quantities, and not quantities themselves, or? + +{:/comment} + +### Trial Classification + +{::comment} + + TODO2: MK do we need this explanation below. Can't we just leave this + section header and then list trial types as is? + +{:/comment} + +When one Trial Result instance is compared to one Search Goal instance, +several relations can be named using short adjectives. + +As trial results do not affect each other, this **Trial Classification** +does not change during the Search. + +{::comment} + + TODO-P0: Is it obvious the adjectives can be combined? + + TODO2: MK **current** and **final** adjectives seem to relate to values + of quantities, and not quantities themselves, or? + +{:/comment} + +#### High-Loss Trial + +A trial with Trial Loss Ratio larger than a Goal Loss Ratio value +is called a **high-loss trial**, with respect to given Search Goal +(or lossy trial, if Goal Loss Ratio is zero). + +#### Low-Loss Trial + +If a trial is not high-loss, it is called a **low-loss trial** +(or even zero-loss trial, if Goal Loss Ratio is zero). + +#### Short Trial + +A trial with Trial Duration shorter than the Goal Final Trial Duration +is called a **short trial** (with respect to the given Search Goal). + +#### Full-Length Trial + +A trial that is not short is called a **full-length** trial. + +Note that this includes Trial Durations larger than Goal Final Trial Duration. + +#### Long Trial + +A trial with Trial Duration longer than the Goal Final Trial Duration +is called a **long trial**. + +{::comment} + + TODO-P0: If used in Logic chapter, add to Glossary and maybe move before full-length. + + TODO-P2: Maybe change fourth goal there to show this better? + + TODO-P0: If not used, delete. + +{:/comment} + +### Load Classification + +{::comment} + + TODO-P0: Turn into a precise definition paragraph. + +{:/comment} + +When the set of all Trial Result instances performed so far +at one Trial Load is compared to one Search Goal instance, +two relations can be named using the concept of a bound. + +In general, such bounds are a current quantity, +even though cases of changing bounds is rare in practice. + +#### Upper Bound + +Definition: + +A Trial Load value is called an Upper Bound if and only if it is classified +as such by [Appendix A: Load Classification](#appendix-a-load-classification) +algorithm for the given Search Goal at the current moment of the Search. + +Discussion: + +In more detail, the set of all Trial Results +performed so far at the Trial Load (and any Trial Duration) +is certain to fail to uphold all the requirements of the given Search Goal, +mainly the Goal Loss Ratio in combination with the Goal Exceed Ratio. +Here "certain to fail" relates to any possible results within the time +remaining till Goal Duration Sum. + +{::comment} + + TODO2: MK not sure above paragraph adds any explanation value whatsover. + It verges into the domain of discussing all possible outcomes and does + nothing to clarify what upper bound is about. And as there is no clear + explanation of upper bound i added one above. + +{:/comment} + +One search goal can have multiple different Trial Load values +classified as its Upper Bounds. +As search progresses and more trials are measured, +any load value can become an Upper Bound. + +Also, a load can stop being an Upper Bound, but that +can only happen when more than Goal Duration Sum of trials are measured +(e.g. because another Search Goal needs more trials at this load). +In that case the load becomes a Lower Bound (see next subsection), +and we say the previous Upper Bound got Invalidated. + +{::comment} + [Medium priority, depends on how many user recommendations we have.] + + With non-zero exceed ratio values, a short high-loss trial may not be enough + to classify a load as the relevant upper bound. + Users MAY apply Goal Duration Sum value lower than Goal Final Trial Duration + to force such classification in hope to save time, + but it is RECOMMENDED not to do so, as in practice + it hurts comparability and repeatability. + +{:/comment} + +{::comment} + [Probably too technical, unless relation to repeatability is found.] + + In general, a load starts as as undecided, then maybe flips to become + an upper bound. MLRsearch stops measuring at that load for this goal, + but it may be forced to measure more for some other search goals, + in which case the load may flip to a lower bound (and back and forth). + + <mark>[VP] TODO: Confirm the load can never flip back to being undecided.</mark> + + Even though the load classification may change during the search, + the goal results are established at the end of the search. + + If the exceed ratio is zero, an upper bound can never flip; + one high-loss trial (even short) is enough to pin the classification. + +{:/comment} + +#### Lower Bound + +Definition: + +A Trial Load value is called a Lower Bound if and only if it is classified +as such by [Appendix A: Load Classification](#appendix-a-load-classification) +algorithm for the given Search Goal at the current moment of the search. + +Discussion: + +{::comment} + + MK: + It is the minimum value in a range being searched, together with Upper + Bound, defining the interval within which MLRsearch operates for + specific Search Goal, iteratively narrowing down to arrive to Search + Result. + + VP: That is wrong in situations with Loss Inversions. + +{:/comment} + +In more detail, the set of all Trial Results +performed so far at the Trial Load (and any Trial Duration) +is certain to uphold all the requirements of the given Search Goal, +mainly the Goal Loss Ratio in combination with the Goal Exceed Ratio. +Here "certain to uphold" relates to any possible results within the time +remaining till Goal Duration Sum. + +{::comment} + + TODO2: MK similar to previous section - not sure above paragraph adds + any explanation value whatsover. It verges into the domain of + discussing all possible outcomes and does nothing to clarify what upper + bound is about. And as there is no clear explanation of upper bound i + added one above. + +{:/comment} + +One search goal can have multiple different Trial Load values +classified as its Lower Bounds. +As search progresses and more trials are measured, +any load value can become a Lower Bound. + +No load can be both an Upper Bound and a Lower Bound for the same Search goal +at the same time, but it is possible for a higher load to be a Lower Bound +while a smaller load is an Upper Bound. + +Also, a load can stop being a Lower Bound, but that +can only happen when more than Goal Duration Sum of trials are measured +(e.g. because another Search Goal needs more trials at this load). +In that case the load becomes an Upper Bound, +and we say the previous Lower Bound got Invalidated. + +## Result Terms + +Before defining the full structure of Controller Output, +it is useful to define the composite quantity called Goal Result. +The following subsections define its attribute first, +before describing the Goal Result quantity. + +There is a correspondence between Search Goals and Goal Results. +Most of the following subsections refer to a given Search Goal, +when defining their terms. +Conversely, at the end of the search, each Search Goal instance +has its corresponding Goal Result instance. + +### Relevant Upper Bound + +Definition: + +The Relevant Upper Bound is the smallest Trial Load value +classified as an Upper Bound for the given Search Goal at the end of the search. + +Discussion: + +If no measured load had enough high-loss trials, +the Relevant Upper Bound MAY be not-existent. +For example, when Max Load is classified as a Lower Bound. + +{::comment} + + TODO-P0: Delete or move: + + TODO2: MK duplicate content explaining the same as above but with + inverse logic. + +{:/comment} + +Conversely, if Relevant Upper Bound exists, +it is not affected by Max Load value. + +### Relevant Lower Bound + +Definition: + +The Relevant Lower Bound is the largest Trial Load value +among those smaller than the Relevant Upper Bound, that got classified +as a Lower Bound for the given Search Goal at the end of the search. + +Discussion: + +If no load had enough low-loss trials, the relevant lower bound +MAY be non-existent. + +Strictly speaking, if the Relevant Upper Bound does not exist, +the Relevant Lower Bound also does not exist. +In a typical case, Max Load is classified as a Lower Bound, +but it is not clear whether a higher value +would be found as a Lower Bound if the search was not limited +by this Max Load value. + +### Conditional Throughput + +Definition: + +Conditional Throughput is a value computed at the Relevant Lower Bound +according to algorithm defined in +[Appendix B: Conditional Throughput](#appendix-b-conditional-throughput). + +Discussion: + +The Relevant Lower Bound is defined only at the end of the search, +and so is the Conditional Throughput. +But the algorithm can be applied at any time on any Lower Bound load, +so the final Conditional Throughput value may appear sooner +than at the end of the search. + +Informally, the Conditional Throughput should be +a typical Trial Forwarding Rate, expected to be seen +at the Relevant Lower Bound of the given Search Goal. + +But frequently it is only a conservative estimate thereof, +as MLRsearch implementations tend to stop gathering more trials +as soon as they confirm the value cannot get worse than this estimate +within the Goal Duration Sum. + +This value is RECOMMENDED to be used when evaluating repeatability +and comparability of different MLRsearch implementations. + +See [Generalized Throughput](#generalized-throughput) for more details. + +{::comment} + [Low priority but useful for comparabuility.] + + <mark>[VP] TODO: Add subsection for Trial Results At Relevant Bounds + as an optional attribute of Goal Result.</mark> + +{:/comment} + +### Goal Results + +MLRsearch specification is based on a set of requirements +for a "regular" result. But in practice, it is not always possible +for such result instance to exist, so also "irregular" results +need to be supported. + +#### Regular Goal Result + +Definition: + +Regular Goal Result is a composite quantity consisting of several attributes. +Relevant Upper Bound and Relevant Lower Bound are REQUIRED attributes, +Conditional Throughput is a RECOMMENDED attribute. +Stopping conditions for the corresponding Search Goal MUST be satisfied. + +Discussion: + +Both relevant bounds MUST exist. + +If the implementation offers Goal Width as a Search Goal attribute, +the distance between the Relevant Lower Bound +and the Relevant Upper Bound MUST NOT be larger than the Goal Width, + +Implementations MAY add their own attributes. + +Test report MUST display Relevant Lower Value, +Displaying Relevant Upper Bound is NOT REQUIRED, but it is RECOMMENDED, +especially if the implementation does not use Goal Width. + +#### Irregular Goal Result + +Definition: + +Irregular Goal Result is a composite quantity. No attributes are required. + +Discussion: + +It is RECOMMENDED to report any useful quantity even if it does not +satisfy all the requirements. For example if Max Load is classified +as a Lower Bound, it is fine to report it as the Relevant Lower Bound, +and compute Conditional Throughput for it. In this case, +only the missing Relevant Upper Bound signals this result instance is irregular. + +Similarly, if both revevant bounds exist, it is RECOMMENDED +to include them as Irregular Goal Result attributes, +and let the Manager decide if their distance is too far for users' purposes. + +If test report displays some Irregular Goal Result attribute values, +they MUST be clearly marked as comming from irregular results. + +The implementation MAY define additional attributes. + +{::comment} + [Useful.] + + <mark>MKP2 [VP] TODO: Also allways-fail. Link to bounds to avoid duplication.</mark> + +{:/comment} + +#### Goal Result + +Definition: + +Goal Result is a composite quantity. Each instance is either a Regular Goal Result +or an Irregular Goal Result. + +Discussion: + +The Manager MUST be able to distinguish whether the instance is regular or not. + +### Search Result + +Definition: + +The Search Result is a single composite object +that maps each Search Goal instance to a corresponding Goal Result instance. + +Discussion: + +Alternatively, the Search Result can be implemented as an ordered list +of the Goal Result instances, matching the order of Search Goal instances. + +The Search Result (as a mapping) +MUST map from all the Search Goal instances present in the Controller Input. + +Identical Goal Result instances MAY be listed for different Search Goals, +but their status as regular or irregular may be different. +For example if two goals differ only in Goal Width value, +and the relevant bound values are close enough according to only one of them. + +{::comment} + [Not important.] + + <mark>[VP] Postponed: API independence, modularity.</mark> + +{:/comment} + +### Controller Output + +Definition: + +The Controller Output is a composite quantity returned from the Controller +to the Manager at the end of the search. +The Search Result instance is its only REQUIRED attribute. + +Discussion: + +MLRsearch implementation MAY return additional data in the Controller Output, +for example number of trials performed and the total Search duration. + +{::comment} + + TODO-P0: "max search time exceeded" flag? + +{:/comment} + +## MLRsearch Architecture + +MLRsearch architecture consists of three main system components: +the Manager, the Controller, and the Measurer. + +The architecture also implies the presence of other components, +such as the SUT and the Tester (as a sub-component of the Measurer). + +Protocols of communication between components are generally left unspecified. +For example, when MLRsearch specification mentions "Controller calls Measurer", +it is possible that the Controller notifies the Manager +to call the Measurer indirectly instead. This way the Measurer implementations +can be fully independent from the Controller implementations, +e.g. programmed in different programming languages. + +### Measurer + +Definition: + +The Measurer is an abstract system component that when called +with a [Trial Input](#trial-input) instance, performs one [Trial ](#trial), +and returns a [Trial Output](#trial-output) instance. + +Discussion: + +This definition assumes the Measurer is already initialized. +In practice, there may be additional steps before the Search, +e.g. when the Manager configures the traffic profile +(either on the Measurer or on its tester sub-component directly) +and performs a warmup (if the test procedure requires one). + +It is the responsibility of the Measurer implementation to uphold +any requirements and assumptions present in MLRsearch specification, +e.g. Trial Forwarding Ratio not being larger than one. + +Implementers have some freedom. +For example [RFC2544] (Section 10) +gives some suggestions (but not requirements) related to +duplicated or reordered frames. +Implementations are RECOMMENDED to document their behavior +related to such freedoms in as detailed a way as possible. + +It is RECOMMENDED to benchmark the test equipment first, +e.g. connect sender and receiver directly (without any SUT in the path), +find a load value that guarantees the Offered Load is not too far +from the Intended Load, and use that value as the Max Load value. +When testing the real SUT, it is RECOMMENDED to turn any big difference +between the Intended Load and the Offered Load into increased Trial Loss Ratio. + +Neither of the two recommendations are made into requirements, +because it is not easy to tell when the difference is big enough, +in a way thay would be dis-entangled from other Measurer freedoms. + +### Controller + +Definition: + +The Controller is an abstract system component +that when called once with a Controller Input instance +repeatedly computes Trial Input instance for the Measurer, +obtains corresponding Trial Output instances, +and eventually returns a Controller Output instance. + +Discussion: + +Informally, the Controller has big freedom in selection of Trial Inputs, +and the implementations want to achieve all the Search Goals +in the shortest expected time. + +The Controller's role in optimizing the overall search time +distinguishes MLRsearch algorithms from simpler search procedures. + +Informally, each implementation can have different stopping conditions. +Goal Width is only one example. +In practice, implementation details do not matter, +as long as Goal Result instances are regular. + +### Manager + +Definition: + +The Manager is an abstract system component that is reponsible for +configuring other components, calling the Controller component once, +and for creating the test report following the reporting format as +defined in [RFC2544] (Section 26). + +Discussion: + +The Manager initializes the SUT, the Measurer (and the Tester if independent) +with their intended configurations before calling the Controller. + +The Manager does not need to be able to tweak any Search Goal attributes, +but it MUST report all applied attribute values even if not tweaked. + +In principle, there should be a "user" (human or CI) +that "starts" or "calls" the Manager and receives the report. +The Manager MAY be able to be called more than once whis way, +thus triggering multiple independent Searches. + +{::comment} + [Not important, unless anybody else asks.] + + <mark>MKP2 The Manager may use the Measurer or other system components + to perform other tests, e.g. back-to-back frames, + as the Controller is only replacing the search from + [RFC2544] (Section 26.1).</mark> + +{:/comment} + +{::comment} + + TODO-P2: Summarize test report requirements here? + +{:/comment} + +## Compliance + +This section discusses compliance relations between MLRsearch +and other test procedures. + +### Test Procedure Compliant with MLRsearch + +Any networking measurement setup where there can be logically delineated +system components and there are abstract components satisfying requirements +for the Measurer, the Controller and the Manager, +is considered to be compliant with MLRsearch specification. + +These components can be seen as abstractions present in any testing procedure. +For example, there can be a single component acting both +as the Manager and the Controller, but as long as values of required attributes +of Search Goals and Goal Results are visible in the test report, +the Controller Input instance and Controller Output instance are implied. + +For example, any setup for conditionally (or unconditionally) +compliant [RFC2544] throughput testing +can be understood as a MLRsearch architecture, +as long as there is enough data to reconstruct the Relevant Upper Bound. +See the next subsection for an equivalent Search Goal. + +Any test procedure that can be understood as (one call to the Manager of) +MLRsearch architecture is said to be compliant with MLRsearch specification. + +{::comment} + + TODO-P0: Delete occurances of "MLRsearch Implementation", review + occurances of "MLRsearch implementation". + +{:/comment} + +### MLRsearch Compliant with RFC2544 + +The following Search Goal instance makes the corresponding Search Result +unconditionally compliant with [RFC2544] (Section 24). + +- Goal Final Trial Duration = 60 seconds +- Goal Duration Sum = 60 seconds +- Goal Loss Ratio = 0% +- Goal Exceed Ratio = 0% + +The latter two attributes, Goal Loss Ratio and Goal Exceed Ratio, +are enough to make the Search Goal conditionally compliant. +Adding the first attribute, Goal Final Trial Duration, +makes the Search Goal unconditionally compliant. + +The second attribute (Goal Duration Sum) only prevents MLRsearch +from repeating zero-loss full-length trials. + +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. + +{::comment} + + TODO-P1: Move the rest into Load Classification Logic chapter. + +{:/comment} + +Non-zero exceed ratio is not strictly disallowed, but it could +needlessly prolong the search when low-loss short trials are present. + +{::comment} + + TODO-P2: Also it would open more questions re Loss Inversion, + but no need to say that anywhere. + +{:/comment} + +### MLRsearch Compliant with TST009 + +One of the alternatives to [RFC2544] is Binary search with loss verification +as described in [TST009] (Section 12.3.3). + +The idea there is to repeat high-loss trials, hoping for zero loss on second try, +so the results are closer to the noiseless end of performance sprectum, +thus more repeatable and comparable. + +Only the variant with "z = infinity" is achievable with MLRsearch. + +{::comment} + [Low priority, unless a short sentence is found.] + + <mark>MKP2 MK note: Shouldn't we add a note about how MLRsearch goes about + addressing the TST009 point related to z, that is "z is threshold of + Lord(r) to override Loss Verification when the count of lost frames is + very high and unnecessary verification trials."? i.e. by have Goal Loss + Ratio. Thoughts?</mark> + +{:/comment} + +For example, for "max(r) = 2" variant, the following Search Goal instance +should be used to get compatible Search Result: + +- Goal Final Trial Duration = 60 seconds +- Goal Duration Sum = 120 seconds +- Goal Loss Ratio = 0% +- Goal Exceed Ratio = 50% + +If the first 60s trial has zero loss, it is enough for MLRsearch to stop +measuring at that load, as even a second high-loss trial +would still fit within the exceed ratio. + +But if the first trial is high-loss, MLRsearch needs to perform also +the second trial to classify that load. +Goal Duration Sum is twice as long as Goal Final Trial Duration, +so third full-length trial is never needed. + +# Further Explanations + +This chapter provides further explanations of MLRsearch behavior, +mainly in comparison to a simple bisection for [RFC2544] Throughput. + +## Binary Search + +A typical binary search implementation for [RFC2544] +tracks only the two tightest bounds. +To start, the search needs both Max Load and Min Load values. +Then, one trial is used to confirm Max Load is an Upper Bound, +and one trial to confirm Min Load is a Lower Bound. + +Then, next Trial Load is chosen as the mean of the current tightest upper bound +and the current tightest lower bound, and becomes a new tightest bound +depending on the Trial Loss Ratio. + +After some number of trials, the tightest lower bound becomes the throughput, +but [RFC2544] does not specify when, if ever, the search should stop. +In practice, the search stops either at some distance +between the tightest upper bound and the tightest lower bound, +or after some number of Trials. + +For a given pair of Max Load and Min Load values, +there is one-to-one correspondence between number of Trials +and final distance between the tightest bounds. +Thus, the search always takes the same time, +assuming initial bounds are confirmed. + +## Stopping Conditions and Precision + +MLRsearch specification requires listing both Relevant Bounds for each +Search Goal, and the difference between the bounds implies +whether the result precision achieved. +Therefore it is not necessary to report the specific stopping condition used. + +MLRsearch implementations may use Goal Width +to allow direct control of result precision, +and indirect control of the search duration. + +Other MLRsearch implementations may use different stopping conditions; +for example based on the search duration, trading off precision control +for duration control. + +Due to various possible time optimizations, there is no longer a strict +correspondence between the overall search duration and Goal Width values. +In practice, noisy SUT performance increases both average search time +and its variance. + +## Loss Ratios and Loss Inversion + +The most obvious difference between MLRsearch and [RFC2544] binary search +is in the goals of the search. +[RFC2544] has a single goal, based on classifying a single full-length trial +as either zero-loss or non-zero-loss. +MLRsearch supports searching for multiple goals at once, +usually differing in their Goal Loss Ratio values. + +### Single Goal and Hard Bounds + +Each bound in [RFC2544] simple binary search is "hard", +in the sense that all further Trial Load values +are smaller than any current upper bound and larger than any current lower bound. + +This is also possible for MLRsearch implementations, +when the search is started with only one Search Goal instance. + +### Multiple Goals and Loss Inversion + +MLRsearch supports multiple goals, making the search procedure +more complicated compared to binary search with single goal, +but most of the complications do not affect the final results much. +Except for one phenomenon: Loss Inversion. + +Depending on Search Goal attributes, Load Classification results may be resistant +to small amounts of [Inconsistent Trial Results](#inconsistent-trial-results). +But for larger amounts, a Load that is classified +as an Upper Bound for one Search Goal +may still be a Lower Bound for another Search Goal. +And, due to this other goal, MLRsearch will probably perform subsequent Trials +at Trial Loads even higher than the original value. + +{::comment} + + TODO-P2: Unify load adjectives: higher/lower xor larger/smaller. => higher/lower. + +{:/comment} + +This introduces questions any many-goals search algorithm has to address. +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 low-loss trials are not considered a lower bound? + +The situation where a smaller load is classified as an Upper Bound, +while a larger load is classified as a Lower Bound (for the same search goal), +is called Loss Inversion. + +Conversely, only single-goal search algorithms can have hard bounds +that shield them from Loss Inversion. + +### Conservativeness and Relevant Bounds + +MLRsearch is conservative when dealing with Loss Inversion: +the Upper Bound is considered real, and the Lower Bound +is considered to be a fluke, at least when computing the final result. + +This is formalized using definitions of +[Relevant Upper Bound](#relevant-upper-bound) and +[Relevant Lower Bound](#relevant-lower-bound). +The Relevant Upper Bound (for specific 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 meet the stopping conditions, the Relevant Bounds are used in the output. + +### Consequences + +The consequence of the way the Relevant Bounds are defined is that +every Trial Result can have an impact +on any current Relevant Bound larger than that Trial Load, +namely by becoming a new Upper Bound. + +This also applies when that trial happens +before that bound could have become current. + +This means if your SUT (or your Traffic Generator) needs a warmup, +be sure to warm it up before starting the Search. + +Also, for MLRsearch implementation, it means it is better to measure +at smaller loads first, so bounds found earlier are less likely +to get invalidated later. + +## Exceed Ratio and Multiple Trials + +The idea of performing multiple Trials at the same Trial Load comes from +a model where some Trial Results (those with high Trial Loss Ratio) are affected +by infrequent effects, causing poor repeatability of [RFC2544] Throughput results. +See the discussion about noiseful and noiseless ends +of the SUT performance spectrum in section [DUT in SUT](#dut-in-sut). +Stable results are closer to the noiseless end of the SUT performance spectrum, +so MLRsearch may need to allow some frequency of high-loss trials +to ignore the rare but big effects near the noiseful end. + +For MLRsearch to perform such Trial Result filtering, it needs +a configuration option to tell how frequent can the "infrequent" big loss be. +This option is called the [Goal Exceed Ratio](#goal-exceed-ratio). +It tells MLRsearch what ratio of trials (more specifically, +what ratio of Trial Effective Duration seconds) +can have a [Trial Loss Ratio](#trial-loss-ratio) +larger than the [Goal Loss Ratio](#goal-loss-ratio) +and still be classified as a [Lower Bound](#lower-bound). + +Zero exceed ratio means all trials must have a Trial Loss Ratio +equal to or smaller than the Goal Loss Ratio. + +When more than one trial is intended to classify a Load, +MLRsearch also needs something that controls the number of trials needed. +Therefore, each goal also has an attribute called Goal Duration Sum. + +The meaning of a [Goal Duration Sum](#goal-duration-sum) is that +when a load has (full-length) trials +whose Trial Effective Durations when summed up give a value at least as big +as the Goal Duration Sum value, +the load is guaranteed to be classified either as an Upper Bound +or a Lower Bound for that Search Goal instance. + +{::comment} + + TODO-P2: Move some discussion on Trial Effective Duration from spec chapter + to around here? Probably no time to dwell on this, delete the todo. + TODO2: my pref is to keep it in spec section + +{:/comment} + +## Short Trials and Duration Selection + +MLRsearch requires each goal to specify its Goal Final Trial Duration. + +Section 24 of [RFC2544] already anticipates possible time savings +when Short Trials are used. + +Any MLRsearch implementation MAY include its own configuration options +which control when and how MLRsearch chooses to use short trial durations. + +While MLRsearch implementations are free to use any logic to select +Trial Input values, comparability between MLRsearch implementations +is only assured when the Load Classification logic +handles any possible set of Trial Results in the same way. + +The presence of short trial results complicates +the load classification logic, see details in +[Load Classification Logic](#load-classification-logic) chapter. + +While the Load Classification algorithm is designed to avoid any unneeded Trials, +for explainability reasons it is RECOMMENDED for users to use +such Controller Input instances that lead to all Trial Duration values +selected by Controller to be the same, +e.g. by setting any Goal Initial Trial Duration to be a single value +also used in all Goal Final Trial Duration attributes. + +{::comment} + + TODO-P0: last statement is confusing. it implies GITD = GFTD, which doesn't make sense to me. + + TODO-P0: below to be removed once Load Classification Logic is done. + +{:/comment} + +In a nutshell, results from short trials +may cause a load to be classified as an upper bound. +This may cause loss inversion, and thus lower the Relevant Lower Bound, +below what would classification say when considering full-length trials only. + +{::comment} + [Important. Keeping compatibility slows search considerably.] + + <mark>Alas, such configurations are usually not compliant with [RFC2544] requirements, + or not time-saving enough.</mark> + + <mark>mk edit note: This statement does not make sense to me. Suggest to remove it.</mark> + +{:/comment} + +## Generalized Throughput + +Due to the fact that testing equipment takes the Intended Load +as an input parameter for a trial measurement, +any load search algorithm needs to deal with Intended Load values internally. + +But in the presence of goals with a non-zero [Goal Loss Ratio](#goal-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 trials and a non-zero goal loss ratio. + +The best example is also the main motivation: hard performance limit. + +### Hard Performance Limit + +Even if bandwidth of the medium allows higher performance, +the SUT interfaces may have their additional own limitations, +e.g. a specific frames-per-second limit on the NIC (a common occurance). + +Ideally, those should be known and provided as [Max Load](#max-load). +But if Max Load is set higher than what the interface can receive or transmit, +there will be a "hard limit" observed in trial results. + +Imagine the hard limit is at hundred million frames per second (100 Mfps), +Max Load is higher, and the goal loss ratio is 0.5%. +If DUT has no additional losses, 0.5% loss ratio will be achieved +at Relevant Lower Bound of 100.5025 Mfps. +But it is not intuitive to report SUT performance as a value that is +larger than the known hard limit. +We need a generalization of RFC2544 throughput, +different from just the Relevant Lower Bound. + +MLRsearch defines one such generalization, +the [Conditional Throughput](#conditional-throughput). +It is the Trial Forwarding Rate from one of the full-length trials +performed at the Relevant Lower Bound. +The algorithm to determine which trial exactly is in +[Appendix B: Conditional Throughput](#appendix-b-conditional-throughput). + +In the hard limit example, 100.5025 Mfps load will still have +only 100.0 Mfps forwarding rate, nicely confirming the known limitation. + +### Performance Variability + +With non-zero Goal Loss Ratio, and without hard performance limits, +low-loss trials at the same Load may achieve different Trial Forwarding Rate +values just due to DUT performance variability. + +By comparing the best case (all Relevant Lower Bound trials have zero loss) +and the worst case (all Trial Loss Ratios at Relevant Lower Bound +are equal to the Goal Loss Ratio), we find the possible Conditional Throughput +values may have up to the Goal Loss Ratio relative difference. + +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. + +Despite this variability, in practice Conditional Throughput behaves better +than Relevant Lower Bound for comparability purposes. + +{::comment} + + TODO-P0: Move the rest into the last chapter. + +{:/comment} + +Conditional Throughput is partially related to load classification. +If a load is classified as a Relevant Lower Bound for a goal, +the Conditional Throughput comes from a trial result, +that is guaranteed to have Trial Loss Ratio no larger than the Goal Loss Ratio. + +{::comment} + [Important only for "design principles" chapter we may never have.] + + <mark>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.</mark> + + <mark>mk edit note: This paragraph adds to the confusion. I would remove + this paragraph, as with the new text above it doesn't seem to add any + value.</mark> + + <mark>[VP] TODO: This is an example of MLRsearch design principles.</mark> + +{:/comment} + +{::comment} + [Useful.] + + <mark>[VP] TODO: Mention somewhere that trending is a specific case + of repeatability/comparability.</mark> + +{:/comment} + +{::comment} + [Important for BMWG. Configurability is bad for comparability.] + + <mark>MKP2 Sadly, different implementations may exhibit their sweet spot of</mark> + <mark>the best repeatability for a given search duration</mark> + <mark>at different goals attribute values, especially concerning</mark> + <mark>any optional goal attributes such as the initial trial duration.</mark> + <mark>Thus, this document does not comment much on which configurations</mark> + <mark>are good for comparability between different implementations.</mark> + <mark>For comparability between different SUTs using the same implementation,</mark> + <mark>refer to configurations recommended by that particular implementation.</mark> + + <mark>MKP2 mk edit note: Isn't this going off on a tangent, hypothesising and + second guessing about different possible implementations. What is the + value of this content to this document? Suggest to remove it.</mark> + +{:/comment} + +# MLRsearch Logic and Example + +This section uses informal language to describe two pieces of MLRsearch logic, +Load Classification and Conditional Throughput, +reflecting formal pseudocode representation present in +[Appendix A: Load Classification](#appendix-a-load-classification) +and [Appendix B: Conditional Throughput](#appendix-b-conditional-throughput). +This is followed by example search. + +{::comment} + + TODO-P1: Move this paragraph to a better place. + TODO-P1: This is an answer to the questions of "why are algorithms this strict"? + TODO-P1: Pose that question somewhere, pose this answer there or in another place. + +{:/comment} + +For repeatability and comparability reasons, it is important that +all implementations of MLRsearch classify the load equivalently, +based on all trials measured at the given load. + +## Load Classification Logic + +Note: For explanation clarity variables are taged as (I)nput, +(T)emporary, (O)utput. + +- Take all Trial Result instances (I) measured at a given load. + +- Full-length high-loss sum (T) is the sum of Trial Effective Duration + values of all full-length high-loss trials (I). +- Full-length low-loss sum (T) is the sum of Trial Effective Duration + values of all full-length low-loss trials (I). +- Short high-loss sum is the sum (T) of Trial Effective Duration values + of all short high-loss trials (I). +- Short low-loss sum is the sum (T) of Trial Effective Duration values + of all short low-loss trials (I). + +- Subceed ratio (T) is One minus the Goal Exceed Ratio (I). +- Exceed coefficient (T) is the Goal Exceed Ratio divided by the subceed + ratio. + +- Balancing sum (T) is the short low-loss sum + multiplied by the exceed coefficient. +- Excess sum (T) is the short high-loss sum minus the balancing sum. +- Positive excess sum (T) is the maximum of zero and excess sum. +- Effective high-loss sum (T) is the full-length high-loss sum + plus the positive excess sum. +- Effective full sum (T) is the effective high-loss sum + plus the full-length low-loss sum. +- Effective whole sum (T) is the larger of the effective full sum + and the Goal Duration Sum. +- Missing sum (T) is the effective whole sum minus the effective full sum. + +- Pessimistic high-loss sum (T) is the effective high-loss sum + plus the missing sum. +- Optimistic exceed ratio (T) is the effective high-loss sum + divided by the effective whole sum. +- Pessimistic exceed ratio (T) is the pessimistic high-loss sum + divided by the effective whole sum. + +- The load is classified as an Upper Bound (O) if the optimistic exceed + ratio is larger than the Goal Exceed Ratio. +- The load is classified as a Lower Bound (O) if the pessimistic exceed + ratio is not larger than the Goal Exceed Ratio. +- The load is classified as undecided (O) otherwise. + +## Conditional Throughput Logic + +Note: For explanation clarity variables are taged as (I)nput, +(T)emporary, (O)utput. + +- Take all Trial Result instances (I) measured at a given Load. + +- Full-length high-loss sum (T) is the sum of Trial Effective Duration + values of all full-length high-loss trials (I). +- Full-length low-loss sum (T) is the sum of Trial Effective Duration + values of all full-length low-loss trials (I). +- Full-length sum (T) is the full-length high-loss sum (I) plus the + full-length low-loss sum (I). + +- Subceed ratio (T) is One minus the Goal Exceed Ratio (I) is called. +- Remaining sum (T) initially is full-lengths sum multiplied by subceed + ratio. +- Current loss ratio (T) initially is 100%. + +- For each full-length trial result, sorted in increasing order by Trial + Loss Ratio: + - If remaining sum is not larger than zero, exit the loop. + - Set current loss ratio to this trial's Trial Loss Ratio (I). + - Decrease the remaining sum by this trial's Trial Effective + Duration (I). + +- Current forwarding ratio (T) is One minus the current loss ratio. +- Conditional Throughput (T) is the current forwarding ratio multiplied + by the Load value. + +{::comment} + TODO-P0: Move somewhere else? MK: I think it's okay to leave it here. +{:/comment} + +By definition, Conditional Throughput logic results in a value +that represents Trial Loss Ratio at most equal to Goal Loss Ratio. + +## SUT Behaviors + +In [DUT in SUT](#dut-in-sut), the notion of noise has been introduced. +In this section we rely on new terms defined since then +to describe possible SUT behaviors more precisely. + +From measurement point of view, noise is visible as inconsistent trial results. +See [Inconsistent Trial Results](#inconsistent-trial-results) for general points +and [Loss Ratios and Loss Inversion](#loss-ratios-and-loss-inversion) +for specifics when comparing different Load values. + +Load Classification and Conditional Throughput apply to a single Load value, +but even the set of Trial Results measured at that Trial Load value +may appear inconsistent. + +As MLRsearch aims to save time, it executes only a small number of Trials, +getting only a limited amount of information about SUT behavior. +It is useful to introduce an "SUT expert" point of view to contrast +with that limited information. + +### Expert Predictions + +Imagine that before the Search starts, a human expert had unlimited time +to measure SUT and obtain all reliable information about it. +The information is not perfect, as there is still random noise influencing SUT. +But the expert is familiar with possible noise events, even the rare ones, +and thus the expert can do probabilistic predictions about future Trial Outputs. + +When several outcomes are possible, +the expert can asses probability of each outcome. + +### Exceed Probability + +When the Controller selects new Trial Duration and Trial Load, +and just before the Measurer starts performing the Trial, +the SUT expert can envision possible Trial Results. + +With respect to a particular Search Goal instance, the possibilities +can be summarized into a single number: Exceed Probability. +It is the probability (according to the expert) that the measured +Trial Loss Ratio will be higher than the Goal Loss Ratio. + +{::comment} + + TODO-P2: Do we need to say small EP means low load? + + TODO-P3: Mention how ER relates to EP here? + + TODO-P2: Tie to Relevant Lower Bound and Conditional Throughput somewhere. + +{:/comment} + +### Trial Duration Dependence + +When comparing Exceed Probability values for the same Trial Load value +but different Trial Duration values, +there are several patterns that commonly occur in practice. + +#### Strong Increase + +Exceed Probability is very small at short durations but very high at full-length. +This SUT behavior is undesirable, and may hint at faulty SUT, +e.g. SUT leaks resources and is unable to sustain the desired performance. + +But this behavior is also seen when SUT uses large amount of buffers. +This is the main reasons users may want to set high Goal Final Trial Duration. + +#### Mild Increase + +Short trials have smaller exceed probability, but the difference is not as high. +This behavior is quite common if the noise contains infrequent but large +loss spikes, as the more performant parts of a full-length trial +are unable to compensate for all the frame loss from a less performant part. + +#### Independence + +Short trials have basically the same Exceed Probability as full-length trials. +This is possible only if loss spikes are small (so other parts can compensate) +and if Goal Loss Ratio is more than zero (otherwise other parts +cannot compensate at all). + +#### Decrease + +Short trials have larger Exceed Probability than full-length trials. +This can be possible only for non-zero Goal Loss Ratio, +for example if SUT needs to "warm up" to best performance within each trial. +Not sommonly seen in practice. + +{::comment} + + TODO-P2: Define loss spikes? Mention loss spikes when discussing noise? + +{:/comment} + +{::comment} + + ### Loss Spikes + + #### Frequent Small Loss Spikes + + #### Rare Big Loss Spikes + +{:/comment} + +## Example Search + +The following example Search is related to +one hypothetical run of a Search test procedure +that has been started with multiple Search Goals. +Several points in time are chosen, in order to show how the logic works, +with specific sets of Trial Result available. +The trial results themselves are not very realistic, as +the intention is to show several corner cases of the logic. + +In all Trials, the Effective Trial Duration is equal to Trial Duration. + +Only one Trial Load is in focus, its value is one million frames per second. +Trial Results at other Trial Loads are not mentioned, +as the parts of logic present here do not depend on those. +In practice, Trial Results at other Load values would be present, +e.g. MLRsearch will look for a Lower Bound smaller than any Upper Bound found. + +In all points in time, only one Search Goal instance is marked as "in focus". +That explains Trial Duration of the new Trials, +but is otherwise unrelated to the logic applied. + +MLRsearch implementations are not required to "focus" on one goal at time, +but this example is useful to show a load can be classified +also for goals not "in focus". + +### Example Goals + +The following four Search Goal instances are selected for the example Search. +Each goal has a readable name and dense code, +the code is useful to show Search Goal attribute values. + +As the variable "exceed coefficient" does not depend on trial results, +it is also precomputed here. + +Goal 1: + + name: RFC2544 + Goal Final Trial Duration: 60s + Goal Duration Sum: 60s + Goal Loss Ratio: 0% + Goal Exceed Ratio: 0% + exceed coefficient: 0% / (100% / 0%) = 0.0 + code: 60f60d0l0e + +Goal 2: + + name: TST009 + Goal Final Trial Duration: 60s + Goal Duration Sum: 120s + Goal Loss Ratio: 0% + Goal Exceed Ratio: 50% + exceed coefficient: 50% / (100% - 50%) = 1.0 + code: 60f120d0l50e + +Goal 3: + + name: 1s final + Goal Final Trial Duration: 1s + Goal Duration Sum: 120s + Goal Loss Ratio: 0.5% + Goal Exceed Ratio: 50% + exceed coefficient: 50% / (100% - 50%) = 1.0 + code: 1f120d.5l50e + +Goal 4: + + name: 20% exceed + Goal Final Trial Duration: 60s + Goal Duration Sum: 60s + Goal Loss Ratio: 0.5% + Goal Exceed Ratio: 20% + exceed coefficient: 20% / (100% - 20%) = 0.25 + code: 60f60d0.5l20e + +The first two goals are important for compliance reasons, +the other two cover less frequent cases. + +### Example Trial Results + +{::comment} + + TODO-P1: Merge this with Point computations so all trial data is localized. + +{:/comment} + +The following six sets of trial results are selected for the example Search. +The sets are defined as points in time, describing which Trial Results +were added since the previous point. + +Each point has a readable name and dense code, +the code is useful to show Trial Output attribute values +and number of times identical results were added. + +Point 1: + + name: first short good + goal in focus: 1s final (1f120d.5l50e) + added Trial Results: 59 trials, each 1 second and 0% loss + code: 59x1s0l + +Point 2: + + name: first short bad + goal in focus: 1s final (1f120d.5l50e) + added Trial Result: one trial, 1 second, 1% loss + code: 59x1s0l+1x1s1l + +Point 3: + + name: last short bad + goal in focus: 1s final (1f120d.5l50e) + added Trial Results: 59 trials, 1 second each, 1% loss each + code: 59x1s0l+60x1s1l + +Point 4: + + name: last short good + goal in focus: 1s final (1f120d.5l50e) + added Trial Results: one trial 1 second, 0% loss + code: 60x1s0l+60x1s1l + +Point 5: + + name: first long bad + goal in focus: TST009 (60f120d0l50e) + added Trial Results: one trial, 60 seconds, 0.1% loss + code: 60x1s0l+60x1s1l+1x60s.1l + +Point 6: + + name: first long good + goal in focus: TST009 (60f120d0l50e) + added Trial Results: one trial, 60 seconds, 0% loss + code: 60x1s0l+60x1s1l+1x60s.1l+1x60s0l + +Comments on point in time naming: + +- When a name contains "short", it means the added trial + had Trial Duration of 1 second, which is Short Trial for 3 of the Search Goals, + but it is a Full-Length Trial for the "1s final" goal. + +- Similarly, "long" in name means the added trial + had Trial Duration of 60 seconds, which is Full-Length Trial for 3 goals + but Long Trial for the "1s final" goal. + +- When a name contains "good" it means the added trial is Low-Loss Trial + for all the goals. + +- When a name contains "short bad" it means the added trial is High-Loss Trial + for all the goals. + +- When a name contains "long bad", it means the added trial + is a High-Loss Trial for goals "RFC2544" and "TST009", + but it is a Low-Loss Trial for the two other goals. + +### Load Classification Computations + +This section shows how Load Classification logic is applied +by listing all temporary values at the specific time point. + +#### Point 1 + +This is the "first short good" point. +Code for available results is: 59x1s0l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 0s | 0s | 0s | 0s +Full-length low-loss sum | 0s | 0s | 59s | 0s +Short high-loss sum | 0s | 0s | 0s | 0s +Short low-loss sum | 59s | 59s | 0s | 59s +Balancing sum | 0s | 59s | 0s | 14.75s +Excess sum | 0s | -59s | 0s | -14.75s +Positive excess sum | 0s | 0s | 0s | 0s +Effective high-loss sum | 0s | 0s | 0s | 0s +Effective full sum | 0s | 0s | 59s | 0s +Effective whole sum | 60s | 120s | 120s | 60s +Missing sum | 60s | 120s | 61s | 60s +Pessimistic high-loss sum | 60s | 120s | 61s | 60s +Optimistic exceed ratio | 0% | 0% | 0% | 0% +Pessimistic exceed ratio | 100% | 100% | 50.833% | 100% +Classification Result | Undecided | Undecided | Undecided | Undecided + +This is the last point in time where all goals have this load as Undecided. + +#### Point 2 + +This is the "first short bad" point. +Code for available results is: 59x1s0l+1x1s1l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 0s | 0s | 1s | 0s +Full-length low-loss sum | 0s | 0s | 59s | 0s +Short high-loss sum | 1s | 1s | 0s | 1s +Short low-loss sum | 59s | 59s | 0s | 59s +Balancing sum | 0s | 59s | 0s | 14.75s +Excess sum | 1s | -58s | 0s | -13.75s +Positive excess sum | 1s | 0s | 0s | 0s +Effective high-loss sum | 1s | 0s | 1s | 0s +Effective full sum | 1s | 0s | 60s | 0s +Effective whole sum | 60s | 120s | 120s | 60s +Missing sum | 59s | 120s | 60s | 60s +Pessimistic high-loss sum | 60s | 120s | 61s | 60s +Optimistic exceed ratio | 1.667% | 0% | 0.833% | 0% +Pessimistic exceed ratio | 100% | 100% | 50.833% | 100% +Classification Result | Upper Bound | Undecided | Undecided | Undecided + +Due to zero Goal Loss Ratio, RFC2544 goal must have mild or strong increase +of exceed probability, so the one lossy trial would be lossy even if measured +at 60 second duration. +Due to zero exceed ratio, one High-Loss Trial is enough to preclude this Load +from becoming a Lower Bound for RFC2544. That is why this Load +is classified as an Upper Bound for RFC2544 this early. + +This is an example how significant time can be saved, compared to 60-second trials. + +#### Point 3 + +This is the "last short bad" point. +Code for available trial results is: 59x1s0l+60x1s1l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 0s | 0s | 60s | 0s +Full-length low-loss sum | 0s | 0s | 59s | 0s +Short high-loss sum | 60s | 60s | 0s | 60s +Short low-loss sum | 59s | 59s | 0s | 59s +Balancing sum | 0s | 59s | 0s | 14.75s +Excess sum | 60s | 1s | 0s | 45.25s +Positive excess sum | 60s | 1s | 0s | 45.25s +Effective high-loss sum | 60s | 1s | 60s | 45.25s +Effective full sum | 60s | 1s | 119s | 45.25s +Effective whole sum | 60s | 120s | 120s | 60s +Missing sum | 0s | 119s | 1s | 14.75s +Pessimistic high-loss sum | 60s | 120s | 61s | 60s +Optimistic exceed ratio | 100% | 0.833% | 50% | 75.417% +Pessimistic exceed ratio | 100% | 100% | 50.833% | 100% +Classification Result | Upper Bound | Undecided | Undecided | Upper Bound + +This is the last point for "1s final" goal to have this Load still Undecided. +Only one 1-second trial is missing within the 120-second Goal Duration Sum, +but its result will decide the classification result. + +The "20% exceed" started to classify this load as an Upper Bound +somewhere between points 2 and 3. + +#### Point 4 + +This is the "last short good" point. +Code for available trial results is: 60x1s0l+60x1s1l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 0s | 0s | 60s | 0s +Full-length low-loss sum | 0s | 0s | 60s | 0s +Short high-loss sum | 60s | 60s | 0s | 60s +Short low-loss sum | 60s | 60s | 0s | 60s +Balancing sum | 0s | 60s | 0s | 15s +Excess sum | 60s | 0s | 0s | 45s +Positive excess sum | 60s | 0s | 0s | 45s +Effective high-loss sum | 60s | 0s | 60s | 45s +Effective full sum | 60s | 0s | 120s | 45s +Effective whole sum | 60s | 120s | 120s | 60s +Missing sum | 0s | 120s | 0s | 15s +Pessimistic high-loss sum | 60s | 120s | 60s | 60s +Optimistic exceed ratio | 100% | 0% | 50% | 75% +Pessimistic exceed ratio | 100% | 100% | 50% | 100% +Classification Result | Upper Bound | Undecided | Lower Bound | Upper Bound + +The one missing trial for "1s final" was low-loss, +half of trial results are low-loss which exactly matches 50% exceed ratio. +This shows time savings are not guaranteed. + +#### Point 5 + +This is the "first long bad" point. +Code for available trial results is: 60x1s0l+60x1s1l+1x60s.1l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 60s | 60s | 60s | 0s +Full-length low-loss sum | 0s | 0s | 120s | 60s +Short high-loss sum | 60s | 60s | 0s | 60s +Short low-loss sum | 60s | 60s | 0s | 60s +Balancing sum | 0s | 60s | 0s | 15s +Excess sum | 60s | 0s | 0s | 45s +Positive excess sum | 60s | 0s | 0s | 45s +Effective high-loss sum | 120s | 60s | 60s | 45s +Effective full sum | 120s | 60s | 180s | 105s +Effective whole sum | 120s | 120s | 180s | 105s +Missing sum | 0s | 60s | 0s | 0s +Pessimistic high-loss sum | 120s | 120s | 60s | 45s +Optimistic exceed ratio | 100% | 50% | 33.333% | 42.857% +Pessimistic exceed ratio | 100% | 100% | 33.333% | 42.857% +Classification Result | Upper Bound | Undecided | Lower Bound | Lower Bound + +As designed for TST009 goal, one Full-Length High-Loss Trial can be tolerated. +120s worth of 1-second trials is not useful, as this is allowed when +Exceed Probability does not depend on Trial Duration. +As Goal Loss Ratio is zero, it is not really possible for 60-second trials +to compensate for losses seen in 1-second results. +But Load Classification logic does not have that knowledge hardcoded, +so optimistic exceed ratio is still only 50%. + +But the 0.1% Trial Loss Ratio is smaller than "20% exceed" Goal Loss Ratio, +so this unexpected Full-Length Low-Loss trial changed the classification result +of this Load to Lower Bound. + +#### Point 6 + +This is the "first long good" point. +Code for available trial results is: 60x1s0l+60x1s1l+1x60s.1l+1x60s0l + +Goal name | RFC2544 | TST009 | 1s final | 20% exceed +--------------------------|-------------|--------------|--------------|-------------- +Goal code | 60f60d0l0e | 60f120d0l50e | 1f120d.5l50e | 60f60d0.5l20e +Full-length high-loss sum | 60s | 60s | 60s | 0s +Full-length low-loss sum | 60s | 60s | 180s | 120s +Short high-loss sum | 60s | 60s | 0s | 60s +Short low-loss sum | 60s | 60s | 0s | 60s +Balancing sum | 0s | 60s | 0s | 15s +Excess sum | 60s | 0s | 0s | 45s +Positive excess sum | 60s | 0s | 0s | 45s +Effective high-loss sum | 120s | 60s | 60s | 45s +Effective full sum | 180s | 120s | 240s | 165s +Effective whole sum | 180s | 120s | 240s | 165s +Missing sum | 0s | 0s | 0s | 0s +Pessimistic high-loss sum | 120s | 60s | 60s | 45s +Optimistic exceed ratio | 66.667% | 50% | 25% | 27.273% +Pessimistic exceed ratio | 66.667% | 50% | 25% | 27.273% +Classification Result | Upper Bound | Lower Bound | Lower Bound | Lower Bound + +This is the Low-Loss Trial the "TST009" goal was waiting for. +This Load is now classified for all goals, the search may end. +Or, more realistically, it can focus on higher load only, +as the three goals will want an Upper Bound (unless this Load is Max Load). + +### Conditional Throughput Computations + +At the end of the hypothetical search, "RFC2544" goal has this load +classified as an Upper Bound, so it is not eligible for Conditional Throughput +calculations. But the remaining three goals calssify this Load as a Lower Bound, +and if we assume it has also became the Relevant Lower Bound, +we can compute Conditional Throughput values for all three goals. + +As a reminder, the Load value is one million frames per second. + +#### Goal 2 + +The Conditional Throughput is computed from sorted list +of Full-Length Trial results. As TST009 Goal Final Trial Duration is 60 seconds, +only two of 122 Trials are considered Full-Length Trials. +One has Trial Loss Ratio of 0%, the other of 0.1%. + +- Full-length high-loss sum is 60 seconds. +- Full-length low-loss sum is 60 seconds. +- Full-length is 120 seconds. +- Subceed ratio is 50%. +- Remaining sum initially is 0.5x12s = 60 seconds. +- Current loss ratio initially is 100%. + +- For first result (duration 60s, loss 0%): + - Remaining sum is larger than zero, not exiting the loop. + - Set current loss ratio to this trial's Trial Loss Ratio which is 0%. + - Decrease the remaining sum by this trial's Trial Effective Duration. + - New remaining sum is 60s - 60s = 0s. +- For second result (duration 60s, loss 0.1%): + - Remaining sum is not larger than zero, exiting the loop. +- Current forwarding ratio was most recently set to 0%. + +- Current forwarding ratio is one minus the current loss ratio, so 100%. +- Conditional Throughput is the current forwarding ratio multiplied by the Load value. +- Conditional Throughput is one million frames per second. + +#### Goal 3 + +The "1s final" has Goal Final Trial Duration of 1 second, +so all 122 Trial Results are considered Full-Length Trials. +They are ordered like this: + + 60 1-second 0% loss trials, + 1 60-second 0% loss trial, + 1 60-second 0.1% loss trial, + 60 1-second 1% loss trials. + +The result does not depend on the order of 0% loss trials. + +- Full-length high-loss sum is 60 seconds. +- Full-length low-loss sum is 180 seconds. +- Full-length is 240 seconds. +- Subceed ratio is 50%. +- Remaining sum initially is 0.5x240s = 120 seconds. +- Current loss ratio initially is 100%. + +- For first 61 results (duration varies, loss 0%): + - Remaining sum is larger than zero, not exiting the loop. + - Set current loss ratio to this trial's Trial Loss Ratio which is 0%. + - Decrease the remaining sum by this trial's Trial Effective Duration. + - New remaining sum varies. +- After 61 trials, we have subtracted 60x1s + 1x60s from 120s, remaining 0s. +- For 62-th result (duration 60s, loss 0.1%): + - Remaining sum is not larger than zero, exiting the loop. +- Current forwarding ratio was most recently set to 0%. + +- Current forwarding ratio is one minus the current loss ratio, so 100%. +- Conditional Throughput is the current forwarding ratio multiplied by the Load value. +- Conditional Throughput is one million frames per second. + +#### Goal 4 + +The Conditional Throughput is computed from sorted list +of Full-Length Trial results. As "20% exceed" Goal Final Trial Duration +is 60 seconds, only two of 122 Trials are considered Full-Length Trials. +One has Trial Loss Ratio of 0%, the other of 0.1%. + +- Full-length high-loss sum is 60 seconds. +- Full-length low-loss sum is 60 seconds. +- Full-length is 120 seconds. +- Subceed ratio is 80%. +- Remaining sum initially is 0.8x120s = 96 seconds. +- Current loss ratio initially is 100%. + +- For first result (duration 60s, loss 0%): + - Remaining sum is larger than zero, not exiting the loop. + - Set current loss ratio to this trial's Trial Loss Ratio which is 0%. + - Decrease the remaining sum by this trial's Trial Effective Duration. + - New remaining sum is 96s - 60s = 36s. +- For second result (duration 60s, loss 0.1%): + - Remaining sum is larger than zero, not exiting the loop. + - Set current loss ratio to this trial's Trial Loss Ratio which is 0.1%. + - Decrease the remaining sum by this trial's Trial Effective Duration. + - New remaining sum is 36s - 60s = -24s. +- No more trials (and also remaining sum is not larger than zero), exiting loop. +- Current forwarding ratio was most recently set to 0.1%. + +- Current forwarding ratio is one minus the current loss ratio, so 99.9%. +- Conditional Throughput is the current forwarding ratio multiplied by the Load value. +- Conditional Throughput is 999 thousand frames per second. + +Due to stricter Goal Exceed Ratio, this Conditional Throughput +is smaller than Conditional Throughput of the other two goals. + +{::comment} + + TODO-P2: Example of long trial being too strict? + + TODO-P2: Unless a set of Search Goals is recommended, comparability is not there. + + TODO-P2: Spell out how MLRsearch addressed the Problems. + +{:/comment} + + +# IANA Considerations + +No requests of IANA. + +# Security Considerations + +Benchmarking activities as described in this memo are limited to +technology characterization of a DUT/SUT using controlled stimuli in a +laboratory environment, with dedicated address space and the constraints +specified in the sections above. + +The benchmarking network topology will be an independent test setup and +MUST NOT be connected to devices that may forward the test traffic into +a production network or misroute traffic to the test management network. + +Further, benchmarking is performed on a "black-box" basis, relying +solely on measurements observable external to the DUT/SUT. + +Special capabilities SHOULD NOT exist in the DUT/SUT specifically for +benchmarking purposes. Any implications for network security arising +from the DUT/SUT SHOULD be identical in the lab and in production +networks. + +# Acknowledgements + +Some phrases and statements in this document were created +with help of Mistral AI (mistral.ai). + +Many thanks to Alec Hothan of the OPNFV NFVbench project for thorough +review and numerous useful comments and suggestions in the earlier versions of this document. + +Special wholehearted gratitude and thanks to the late Al Morton for his +thorough reviews filled with very specific feedback and constructive +guidelines. Thank you Al for the close collaboration over the years, +for your continuous unwavering encouragement full of empathy and +positive attitude. Al, you are dearly missed. + +# Appendix A: Load Classification + +This section specifies how to perform the load classification. + +Any Trial Load value can be classified, +according to a given [Search Goal](#search-goal). + +The algorithm uses (some subsets of) the set of all available trial results +from trials measured at a given intended load at the end of the search. +All durations are those returned by the Measurer. + +The block at the end of this appendix holds pseudocode +which computes two values, stored in variables named +`optimistic_is_lower` and `pessimistic_is_lower`. + +{::comment} + [We have other section re optimistic. Not going to talk about variable naming here.] + + <mark>MKP2 mk edit note: Need to add the description of what + the `optimistic` and `pessimistic` variables represent. + Or a reference to where this is described + e.g. in [Single Trial Duration](#single-trial-duration) section.</mark> + +{:/comment} + +The pseudocode happens to be valid Python code. + +If values of both variables are computed to be true, the load in question +is classified as a lower bound according to the given Search Goal. +If values of both variables are false, the load is classified as an upper bound. +Otherwise, the load is classified as undecided. + +The pseudocode expects the following variables to hold the following values: + +- `goal_duration_sum`: The duration sum value of the given Search Goal. + +- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. + +- `full_length_low_loss_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. + +- `full_length_high_loss_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. + +- `short_low_loss_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. + +- `short_high_loss_sum`: Sum of durations across trials with trial duration + shorter than the goal final trial duration and with a Trial Loss Ratio + higher than the Goal Loss Ratio. + +The code works correctly also when there are no trial results at a given load. + +~~~ python +exceed_coefficient = goal_exceed_ratio / (1.0 - goal_exceed_ratio) +balancing_sum = short_low_loss_sum * exceed_coefficient +positive_excess_sum = max(0.0, short_high_loss_sum - balancing_sum) +effective_high_loss_sum = full_length_high_loss_sum + positive_excess_sum +effective_full_length_sum = full_length_low_loss_sum + effective_high_loss_sum +effective_whole_sum = max(effective_full_length_sum, goal_duration_sum) +quantile_duration_sum = effective_whole_sum * goal_exceed_ratio +pessimistic_high_loss_sum = effective_whole_sum - full_length_low_loss_sum +pessimistic_is_lower = pessimistic_high_loss_sum <= quantile_duration_sum +optimistic_is_lower = effective_high_loss_sum <= quantile_duration_sum +~~~ + +# Appendix B: Conditional Throughput + +This section specifies how to compute Conditional Throughput, as referred to in section [Conditional Throughput](#conditional-throughput). + +Any intended load value can be used as the basis for the following computation, +but only the Relevant Lower Bound (at the end of the search) +leads to the value called the Conditional Throughput for a given Search Goal. + +The algorithm uses (some subsets of) the set of all available trial results +from trials measured at a given intended load at the end of the search. +All durations are those returned by the Measurer. + +The block at the end of this appendix holds pseudocode +which computes a value stored as variable `conditional_throughput`. + +{::comment} + [CT is CT. But text could make more obvious.] + + <mark>MKP2 mk edit note: Need to add the description of what does + the `conditional_throughput` variable represent. + Or a reference to where this is described + e.g. in [Conditional Throughput](#conditional-throughput) section.</mark> + +{:/comment} + +The pseudocode happens to be valid Python code. + +The pseudocode expects the following variables to hold the following values: + +- `goal_duration_sum`: The duration sum value of the given Search Goal. + +- `goal_exceed_ratio`: The exceed ratio value of the given Search Goal. + +- `full_length_low_loss_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. + +- `full_length_high_loss_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. + +- `full_length_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 +full_length_sum = full_length_low_loss_sum + full_length_high_loss_sum +whole_sum = max(goal_duration_sum, full_length_sum) +remaining = whole_sum * (1.0 - goal_exceed_ratio) +quantile_loss_ratio = None +for trial in full_length_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) +~~~ + +# Index + +{::comment} + + TODO-P2: There are long lines. + +{:/comment} + +- Bound: Lower Bound or Upper Bound. +- Bounds: Lower Bound and Upper Bound. +- Conditional Throughput: defined in [Conditional Throughput](#conditional-throughput), discussed in [Generalized Throughput](#generalized-throughput). +- Controller: introduced in [Overview ](#overview), defined in [Controller ](#controller). +- Controller Input: defined in [Controller Input](#controller-input). +- Controller Output: defined in [Controller Output](#controller-output). +- Full-Length Trial: defined in [Full-Length Trial](#full-length-trial). +- Goal Duration Sum: defined in [Goal Duration Sum](#goal-duration-sum), discussed in [Exceed Ratio and Multiple Trials](#exceed-ratio-and-multiple-trials). +- Goal Exceed Ratio: defined in [Goal Exceed Ratio](#goal-exceed-ratio), discussed in [Exceed Ratio and Multiple Trials](#exceed-ratio-and-multiple-trials). +- Goal Final Trial Duration: defined in [Goal Final Trial Duration](#goal-final-trial-duration). +- Goal Initial Trial Duration: defined in [Goal Initial Trial Duration](#goal-initial-trial-duration). +- Goal Loss Ratio: defined in [Goal Loss Ratio](#goal-loss-ratio). +- Goal Result: defined in [Goal Result](#goal-result). +- Goal Width: defined in [Goal Width](#goal-width). +- Exceed Probability: defined in [Exceed Probability](#exceed-probability) +- High-Loss Trial: defined in [High-Loss Trial](#high-loss-trial). +- Intended Load: defined in [RFC2285] (Section 3.5.1). +- Irregular Goal Result: defined in [Irregular Goal Result](#irregular-goal-result). +- Load: introduced in [Trial Load](#trial-load). +- Load Classification: Introduced in [Overview ](#overview), defined in [Load Classification](#load-classification), discussed in [Load Classification Logic](#load-classification-logic). +- Loss Inversion: Situation introduced in [Inconsistent Trial Results](#inconsistent-trial-results), defined in [Loss Ratios and Loss Inversion](#loss-ratios-and-loss-inversion). +- Low-Loss Trial: defined in [Low-Loss Trial](#low-loss-trial). +- Lower Bound: defined in [Lower Bound](#lower-bound). +- Manager: introduced in [Overview ](#overview), defined in [Manager ](#manager). +- Max Load: defined in [Max Load](#max-load). +- Measurer: introduced in [Overview ](#overview), defined in [Meaurer ](#measurer). +- Min Load: defined in [Min Load](#min-load). +- MLRsearch Specification: introduced in [Purpose and Scope](#purpose-and-scope) +and in [Overview ](#overview), defined in [Test Procedure Compliant with MLRsearch](#test-procedure-compliant-with-mlrsearch). +- MLRsearch Implementation: defined in [Test Procedure Compliant with MLRsearch](#test-procedure-compliant-with-mlrsearch). +- Offered Load: defined in [RFC2285] (Section 3.5.2). +- Regular Goal Result: defined in [Regular Goal Result](#regular-goal-result). +- Relevant Bound: Relevant Lower Bound or Relevant Upper Bound. +- Relevant Bounds: Relevant Lower Bound and Relevant Upper Bound. +- Relevant Lower Bound: defined in [Relevant Lower Bound](#relevant-lower-bound), discussed in [Conservativeness and Relevant Bounds](#conservativeness-and-relevant-bounds). +- Relevant Upper Bound: defined in [Relevant Upper Bound](#relevant-upper-bound). +- Search: defined in [Overview ](#overview). +- Search Duration: introduced in [Purpose and Scope](#purpose-and-scope) and in [Long Search Duration](#long-search-duration), discussed in [Stopping Conditions and Precision](#stopping-conditions-and-precision). +- Search Goal: defined in [Search Goal](#search-goal). +- Search Result: defined in [Search Result](#search-result). +- Short Trial: defined in [Short Trial](#short-trial). +- Throughput: defined in [RFC1242] (Section 3.17), Methodology specified in [RFC2544] (Section 26.1). +- Trial: defined in [Trial ](#trial). +- Trial Duration: defined in [Trial Duration](#trial-duration). +- Trial Effective Duration: defined in [Trial Effective Duration](#trial-effective-duration). +- Trial Forwarding Rate: defined in [Trial Forwarding Rate](#trial-forwarding-rate). +- Trial Forwarding Ratio: defined in [Trial Forwarding Ratio](#trial-forwarding-ratio). +- Trial Input: defined in [Trial Input](#trial-input). +- Trial Loss Ratio: defined in [Trial Loss Ratio](#trial-loss-ratio). +- Trial Load: defined in [Trial Load](#trial-load). +- Trial Output: defined in [Trial Output](#trial-output). +- Trial Result: defined in [Trial Result](#trial-result). +- Upper Bound: defined in [Upper Bound](#upper-bound). + +{::comment} + + - Test Procedure: defined in [RFC2544] (Section 26), TODO-P3: That lists several procedures in subsection, + but does not define what "a test procedure" is. + - Test Report: defined in [RFC2544] (Section 26), TODO-P3: Lists reporting formats without actually defining what the report is. + - Tester: defined in [RFC2544] (Section 6), TODO-P3: Not used enough to be in Glossary. + +{:/comment} + +--- back + +{::comment} + [Final checklist.] + + <mark>[VP] Final Checks. Only mark as done when there are no active todos above.</mark> + + <mark>[VP] Rename chapter/sub-/section to better match their content.</mark> + + <mark>MKP3 [VP] TODO: Recheck the definition dependencies go bottom-up.</mark> + + <mark>[VP] TODO: Unify external reference style (brackets, spaces, section numbers and names).</mark> + + <mark>MKP2 [VP] TODO: Capitalization of New Terms: useful when editing and reviewing, + but I still vote to remove capitalization before final submit, + because all other RFCs I see only capitalize due to being section title.</mark> + + <mark>[VP] TODO: If time permits, keep improving formal style (e.g. using AI).</mark> + +{:/comment} diff --git a/docs/ietf/process.txt b/docs/ietf/process.txt index 128c31bff1..f1545e96e8 100644 --- a/docs/ietf/process.txt +++ b/docs/ietf/process.txt @@ -14,15 +14,18 @@ Commands to convert RFC text from .md (so I do not need to search next time). -Hints: https://www.rubydoc.info/gems/kramdown-rfc2629/ +Hints: ++ https://www.rubydoc.info/gems/kramdown-rfc2629/ ++ https://github.com/cabo/kramdown-rfc ++ https://www.rfc-editor.org/materials/FAQ-xml2rfcv3.html Initial: $ sudo aptitude install ruby-rubygems -$ sudo gem install kramdown-rfc2629 +$ sudo gem install kramdown-rfc $ kdrfc --version Main: -$ kdrfc draft-ietf-bmwg-mlrsearch-06.md +$ kdrfc draft-ietf-bmwg-mlrsearch-08.md If that complains, do it manually at https://author-tools.ietf.org/ |