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diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md new file mode 100644 index 0000000000..4db8506131 --- /dev/null +++ b/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md @@ -0,0 +1,1479 @@ +--- +title: Multiple Loss Ratio Search +abbrev: MLRsearch +docname: draft-ietf-bmwg-mlrsearch-04 +date: 2023-07-10 + +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: 2022-11 + PyPI-MLRsearch: + target: https://pypi.org/project/MLRsearch/0.4.0/ + title: "MLRsearch 0.4.0, Python Package Index" + date: 2021-04 + +--- abstract + +This document proposes improvements to [RFC2544] throughput search by +defining a new methodology called Multiple Loss Ratio search +(MLRsearch). The main objectives for MLRsearch are to minimize the +total test duration, search for multiple loss ratios and improve +results repeatibility and comparability. + +The main motivation behind MLRsearch is the new set of challenges and +requirements posed by testing Network Function Virtualization +(NFV) systems and other software based network data planes. + +MLRsearch offers several ways to address these challenges, giving user +configuration options to select their preferred way. + +--- middle + +{::comment} + As we use kramdown to convert from markdown, + we use this way of marking comments not to be visible in rendered draft. + https://stackoverflow.com/a/42323390 + If other engine is used, convert to this way: + https://stackoverflow.com/a/20885980 +{:/comment} + +# Purpose and Scope + +The purpose of this document is to describe Multiple Loss Ratio search +(MLRsearch), a throughput search methodology optimized for software +DUTs. + +Applying vanilla [RFC2544] throughput bisection to software DUTs +results in a number of problems: + +- Binary search takes too long as most of trials are done far from the + eventually found throughput. +- The required final trial duration (and pauses between trials) also + prolong the overall search duration. +- Software DUTs show noisy trial results (noisy neighbor problem), + leading to big spread of possible discovered throughput values. +- Throughput requires loss of exactly zero packets, but the industry + frequently allows for small but non-zero losses. +- The definition of throughput is not clear when trial results are + inconsistent. + +MLRsearch aims to address these problems by applying the following set +of enhancements: + +- Allow searching for multiple search goals, with differing goal loss ratios. + - Each trial result can affect any search goal in principle + (trial reuse). +- Multiple preceding targets for each search goal, earlier ones need + to spend less time on trials. + - Earlier targets also aim at 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. + - Loss ratios goals are handled in an order that minimizes the chance + of interference from later trials to earlier goals. +- Apply several load selection heuristics to save even more time + by trying hard to avoid unnecessarily narrow bounds. + +MLRsearch configuration options are flexible enough to +support both conservative settings (unconditionally compliant with [RFC2544], +but longer search duration and worse repeatability) and aggressive +settings (shorter search duration and better repeatability but not +compliant with [RFC2544]). + +No part of [RFC2544] is intended to be obsoleted by this document. + +# Terminology + +When a subsection is defining a term, the first paragraph +acts as a definition. Other paragraphs are treated as a description, +they provide additional details without being needed to define the term. + +Definitions should form a directed acyclic graph of dependencies. +If a section contains subsections, the section definition +may depend on the subsection definitions. +Otherwise, any definition may depend on preceding definitions. +In other words, if the section definition were to come after subsections, +there would be no forward dependencies for people reading just definitions +from start to finish. + +Descriptions provide motivations and explanations, +they frequently reference terms defined only later. +Motivations in section descriptions are the reason +why section text comes before subsection text. + +## General notions + +General notions are the terms defined in this section. + +It is useful to define the following notions +before delving into MLRsearch architecture, +as the notions appear in multiple places +with no place being special enough to host definition. + +### General and specific quantities + +General quantity is a quantity that may appear multiple times +in MLRsearch specification, perhaps each time in a different role. +The quantity when appearing in a single role is called +a specific quantity. + +It is useful to define the general quantity, +so definitions of specific quantities may refer to it. +We say a specific quantity is based on a general quantity, +if the specific quantity definition refers to and +relies on the general quantity definition. + +It is natural to name specific quantities by adding an adjective +(or a noun) to the name of the general quantity. +But existing RFCs typically explicitly define a term acting +in a specific role, so the RFC name directly refers to a specific +quantity, while the corresponding general quantity +is defined only implicitly. +Therefore this documents defines general quantities explicitly, +even if the same term already appears in an RFC. + +In practice, it is required to know which unit of measurement +is used to accompany a numeric value of each quantity. +The choice of a particular unit of measurement is not important +for MLRsearch specification though, so specific units +mentioned in this document are just examples or recommendations, +not requirements. + +When reporting, it is REQUIRED to state the units used. + +### Composite + +A composite is a set of named attributes. +Each attribute is either a specific quantity or a composite. + +MLRsearch specification frequently groups multiple specific quantities +into a composite. Description of such a composite brings an insight +to motivations why this or other terms are defined as they are. +Such insight will be harder to communicate +with the specific quantities alone. + +Also, it simplifies naming of specific quantities, as they usually can +share a noun or adjective referring to their common composite. +Most of relations between composites and their specific quantities +can be described using plain English. + +Perhaps the only exception involves referring to specific quantities +as attributes. For example if there is a composite called 'target', +and one of its specific quantities is 'target width' defined using +a general quantity 'width', we can say 'width is one of target attributes'. + +### SUT + +As defined in RFC 2285: +The collective set of network devices to which stimulus is offered +as a single entity and response measured. + +While RFC 2544 mostly refers to DUT as a single +(network interconnecting) device, section 19 makes it clear +multiple DUTs can be treated as a single system, +so most of RFC 2544 also applies to testing SUT. + +MLRsearch specification only refers to SUT (not DUT), +even if it consists of just a single device. + +### Trial + +A trial is the part of test described in RFC 2544 section 23. + +When traffic has been sent and SUT response has been observed, +we say the trial has been performed, or the trial has been measured. +Before that happens, multiple possibilities for upcoming trial +may be under consideration. + +### Load + +Intended, constant load for a trial, usually in frames per second. + +Load is the general quantity implied by Constant Load of RFC 1242, +Data Rate of RFC 2544 and Intended Load of RFC 2285. +All three specify this value applies to one (input or output) interface, +so we can talk about unidirectional load also +when bidirectional or multi-port traffic is applied. + +MLRsearch does not rely on this distinction, it works also if +the load values correspond to an aggregate rate +(sum over all SUT tested input or output interface unidirectional loads), +as long as all loads share the same semantics. + +Several RFCs define useful quantities based on Offered Load +(instead of Intended Load), but MLRsearch specification +works only with (intended) load. Those useful quantities +still serve as motivations for few specific quantities used in MLRsearch +specification. + +MLRsearch assumes most load values are positive. +For some (but not all) specific quantities based on load, +zero may also be a valid value. + +### Duration + +Intended duration of the traffic for a trial, usually in seconds. + +This general quantity does not include any preparation nor waiting +described in section 23 of RFC 2544. +Section 24 of RFC 2544 places additional restrictions on duration, +but those restriction apply only to some of the specific quantities based +on duration. + +Duration is always positive in MLRsearch. + +### Duration sum + +For a specific set of trials, this is the sum of their durations. + +Some of specific quantities based on duration sum are derived quantities, +without a specific set of trials to sum their durations. + +Duration sum is never negative in MLRsearch. + +### Width + +General quantity defined for an ordered pair (lower and higher) +of load values, which describes a distance between the two values. + +The motivation for the name comes from binary search. +The binary search tries to approximate an unknown value +by repeatedly bisecting an interval of possible values, +until the interval becomes narrow enough. +Width of the interval is a specific quantity +and the termination condition compares that +to another specific quantity acting as the threshold. +The threshold value does not have a specific interval associated, +but corresponds to a 'size' of the compared interval. +As size is a word already used in definition of frame size, +a more natural word describing interval is width. + +The MLRsearch specification does use (analogues of) upper bound +and lower bound, but does not actually need to talk about intervals. +Still, the intervals are implicitly there, so width is the natural name. + +Actually, there are two popular options for defining width. +Absolute width is based on load, the value is the higher load +minus the lower load. +Relative width is dimensionless, the value is the absolute width +divided by the higher load. As intended loads for trials are positive, +relative width is between 0.0 (including) and 1.0 (excluding). + +Relative width as a threshold value may be useful for users +who do not presume what is the typical performance of SUT, +but absolute width may be a more familiar concept. + +MLRsearch specification does not prescribe which width has to be used, +but widths MUST be either all absolute or all relative, +and it MUST be clear from report which option was used +(it is implied from the unit of measurement of any width value). + +### Loss ratio + +The loss ratio is a general quantity, dimensionless floating point value +assumed to be between 0.0 and 1.0, both including. +It is computed as the number of frames forwarded by SUT, divided by +the number of frames that should have been forwarded during the trial. + +If the number of frames that should have been forwarded is zero, +the loss ratio is considered to be zero +(but it is better to use high enough loads to prevent this). + +Loss ratio is basically the same quantity as Frame Loss Rate of RFC 1242, +just not expressed in percents. + +RFC1242 Frame Loss Rate: +Percentage of frames that should have been forwarded +by a network device under steady state (constant) +load that were not forwarded due to lack of +resources. + +(RFC2544 restricts Frame Loss Rate to a type of benchmark, +for loads 100% of 'maximum rate', 90% and so on.) + +### Exceed ratio + +This general quantity is a dimensionless floating point value, +defined using two duration sum quantities. +One duration sum is referred to as the good duration sum, +the other is referred to as the bad duration sum. +The exceed ratio value is computed as the bad duration sum value +divided by the sum of the two sums. If both sums are zero, +the exceed ratio is undefined. + +As there are no negative duration sums in MLRsearch, +exceed ratio values are between 0.0 and 1.0 (both including). + +## Architecture + +MLRsearch architecture consists of three main components: +the manager, the controller and the measurer. + +The search algorithm is implemented in the controller, +and it is the main focus of this document. + +Most implementation details of the manager and the measurer are +out of scope of this document, except when describing +how do those components interface with the controller. + +### Manager + +The manager is the component that initializes SUT, traffic generator +(called tester in RFC 2544), the measurer and the controller +with intended configurations. It then handles the execution +to the controller and receives its result. + +Managers can range from simple CLI utilities to complex +Continuous Integration systems. From the controller point of view +it is important that no additional configuration (nor warmup) +is needed for SUT and the measurer to perform trials. + +The interface between the manager and the controller +is defined in the controller section. + +One execution of the controller is called a search. +Some benchmarks may execute multiple searches on the same SUT +(for example when confirming the performance is stable over time), +but in this document only one invocation is concerned +(others may be understood as the part of SUT preparation). + +Creation of reports of appropriate format can also be understood +as the responsibility of the manager. This document places requirements +on which information has to be reported. + +### Measurer + +The measurer is the component which performs one trial +as described in RFC 2544 section 23, when requested by the controller. + +From the controller point of view, it is a function that accepts +trial input and returns trial output. + +This is the only way the controller can interact with SUT. +In practice, the measurer has to do subtle decisions +when converting the observed SUT behavior into a single +trial loss ratio value. For example how to deal with +out of order frames or duplicate frames. + +On software implementation level, the measurer is a callable, +injected by the manager into the controller instance. + +The act of performing one trial (act of turning trial input +to trial output) is called a measurement, or trial measurement. +This way we can talk about trials that were measured already +and trials that are merely planned (not measured yet). + +#### Trial input + +The load and duration to use in an upcoming trial. + +This is a composite. + +Other quantities needed by the measurer are assumed to be constant +and set up by the manager before search starts (see traffic profile), +so they do not count as trial input attributes. + +##### Trial load + +Trial load is the intended load for the trial. + +This is a specific quantity based on load, +directly corresponding to RFC 2285 intended load. + +##### Trial duration + +Trial duration is the intended duration for the trial. + +This is a specific quantity based on duration, so it specifies +only the traffic part of the trial, not the waiting parts. + +#### Traffic profile + +Any other configuration values needed by the measurer to perform a trial. + +The measurer needs both trial input and traffic profile to perform the trial. +As trial input contains the only values that vary during one the search, +traffic profile remains constant during the search. + +Traffic profile when understood as a composite is REQUIRED by RFC 2544 +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.) + +#### Trial ouput + +A composite consisting of trial loss ratio +and trial forwarding rate. + +Those are the only two specific quantities (among other quantities +possibly measured in the trial, for example offered load) +that are important for MLRsearch. + +##### Trial loss ratio + +Trial loss ratio is a specific quantity based on loss ratio. +The value is related to a particular measured trial, +as measured by the measurer. + +##### Trial forwarding rate + +Trial forwarding rate is a derived quantity. +It is computed as one minus trial loss ratio, +that multiplied by trial load. + +Despite the name, the general quantity this specific quantity +corresponds to is load (not rate). +The name is inspired by RFC 2285, which defines Forwarding Rate +specific to one output interface. + +As the definition of loss ratio is not neccessarily per-interface +(one of details left for the measurer), using the definition above +(instead of RFC 2285) makes sure trial forwarding rate +is always between zero and the trial load (both including). + +#### Trial result + +Trial result is a composite consisting of trial input attributes +and trial output attributes. + +Those are all specific quantites related to a measured trial MLRsearch needs. + +While distinction between trial input and output is important +when defining the interface between the controller and the measurer, +it is easier to talk about trial result +when describing how measured trials influence the controller behavior. + +### Controller + +The component of MLRsearch architecture that calls the measurer +and returns conditional throughputs to the manager. + +This component implements the search algorithm, +the main content of this document. + +Contrary to Throughput as defined in RFC 1242, +the definition of conditional throughput is quite sensitive +to the controller input (as provided by the manager), +and its full definition needs several terms +which would otherwise be hidden as internals of the controller +implementation. + +The ability of conditional throughput to be less sensitive +to performance variance, and the ability of the controller +to find conditional throughputs for multiple search goals +within one search (and in short overall search time) +are strong enough motivations for the need of increased complexity. + +### Controller input + +A composite of max load, min load, and a set of search goals. + +The search goals (as elements of the set of search goals) +are usually not named and unordered. + +It is fine if all search goals of the set have the same value +of a particular attribute. In that case, the common value +may be treated as a global attribute (similarly to max and min load). + +The set of search goals MUST NOT be empty. +Two search goals within the set MUST differ in at least one attribute. +The manager MAY avoid both issues by presenting empty report +or de-duplicating the search goals, but it is RECOMMENDED +for the manager to raise an error to its caller, +as the two conditions suggest the test is improperly configured. + +#### Max load + +Max load is a specific quantity based on load. +No trial load is ever higher than this value. + +RFC 2544 section 20 defines maximum frame rate +based on theoretical maximum rate for the frame size on the media. +RFC 2285 section 3.5.3 specifies Maximum offered load (MOL) +which may be lower than maximum frame rate. +There may be other limitations preventing high loads, +for examples resources available to traffic generator. + +The manager is expected to provide a value that is not greater +than any known limitation. Alternatively, the measurer +is expected to work at max load, possibly reporting as lost +any frames that were not able to leave Traffic Generator. + +From the controller point of view, this is merely a global upper limit +for any trial load candidates. + +#### Min load + +Min load is a specific quantity based on load. +No trial load is ever lower than this value. + +The motivation of this quantity is to prevent trials +with too few frames sent to SUT. + +Also, practically if a SUT is able to reach only very small +forwarding rates (min load indirectly serves as a threshold for how small), +it may be considered faulty (or perhaps the test is misconfigured). + +#### Search goal + +A composite of 7 attributes (see subsections). + +If not otherwise specified, 'goal' always refers to a search goal +in this document. + +The controller input may contain multiple search goals. +The name Multiple Loss Ratio search was created back when +goal loss ratio was the only attribute allowed to vary between goals. + +Each goal will get its conditional throughput discovered +and reported at the end of the search. + +The definitions of the 7 attributes are not very informative by themselves. +Their motivation (and naming) becomes more clear +from the impact they have on conditional throughput. + +##### Goal loss ratio + +A specific quantity based on loss ratio. +A threshold value for trial loss ratios. +MUST be lower than one. + +Trial loss ratio values will be compared to this value, +a trial will be considered bad if its loss ratio is higher than this. + +For example, RFC 2544 throughput has goal loss ratio of zero, +a trial is bad once a sigle frame is lost. + +Loss ratio of one would classify each trial as good (regardless of loss), +which is not useful. + +##### Goal initial trial duration + +A specific quantity based on duration. +A threshold value for trial durations. +MUST be positive. + +MLRsearch is allowed to use trials as short as this when focusing +on this goal. +The conditional throughput may be influenced by shorter trials, +(measured when focusing on other search goals). + +{::comment} + FIXME: Should shorter trials be explicitly ignored? +{:/comment} + +##### Goal final trial duration + +A specific quantity based on duration. +A threshold value for trial durations. +MUST be no smaller than goal initial trial duration. + +MLRsearch is allowed to use trials as long as this when focusing +on this goal. If more data is needed, repeated trials +at the same load and duration are requested by the controller. + +##### Goal min duration sum + +A specific quantity based on duration sum. +A threshold value for a particular duration sum. + +MLRsearch requires at least this amount of (effective) trials +for a particular load to become part of MLRsearch outputs. + +It is possible (though maybe not prectical) for goal min duration sum +to be smaller than goal final trial duration. + +In practice, the sum of durations actually spent on trial measurement +can be smaller (when trial results are quite one-sided) or even larger +(in presence of shorter-than-final trial duration results at the same load). + +If the sum of all (good and bad) long trials is at least this, +and there are no short trials, then the load is guaranteed +to be classified as either an upper or a lower bound. + +In some cases, the classification is known sooner, +when the 'missing' trials cannot change the outcome. + +When short trials are present, the logic is more complicated. + +##### Goal exceed ratio + +A specific quantity based on exceed ratio. +A threshold value for particulat sets of trials. + +An attribute used for classifying loads into upper and lower bounds. + +If the duration sum of all (current duration) trials is at least +min duration sum, and more than this percentage of the duration sum +comes from bad trials, this load is an upper bound. + +If there are shorter duration trials, the logic is more complicated. + +##### Goal width + +A specific quantity based on width. +A threshold value for a particular width. +MUST be positive. + +This defines the exit condition for this search goal. + +Relevant bounds (of the final target) need to be this close +before conditional throughput can be reported. + +##### Preceding targets + +A non-negative integer affecting the behavior of the controller. + +How many additional non-final targets to add. +Each next preceding target has double width +and min duration sum geometrically closer to initial trial duration. + +The usage of preceding targets is an important source +of MLRsearch time savings (compared to simpler search algorithms). + +Having this value configurable lets the manager +tweak the overall search duration based on presumed knowledge +of SUT performance stability. + +### Controller internals + +Terms not directly corresponding to the controller's input nor output, +but needed indirectly as dependencies of the conditional throughput +definition. + +Following these definitions specifies virtually all of the controller +(MLRsearch algorithm) logic. + +#### Pre-initial trials + +Up to three special trials executed at the start of the search. +The first trial load is max load, +subsequent trial load are computed from preceding trial +forwarding rate. + +The main loop of the controller logic needs at least one trial result, +and time is saved if the trial results are close to future conditional +throughput values. + +The exact way to compute load for second and third trial +(and whether even measure second or third trial) +are not specified here, as the implementation details +have negligible effect on the reported conditional throughput. + +{::comment} + TODO: Still, recommend something like this: + Loads need to fit several different initial targets at once. + Duration is the largest among initial trial durations, + loads are computed from forwarding rate an smallest loss ratio goal. + Also, the initial target current width is set based on these. +{:/comment} + +#### Search target + +A composite of 5 specific quantites (see subsections). +Frequently called just target. + +Similar to (but distinct from) the search goal. + +Each search goal prescribes a final target, +probably with a chain of preceding targets. + +More details in the Derived targets section. + +##### Target loss ratio + +Same as loss ratio of the corresponding goal. + +##### Target exceed ratio + +Same as exceed ratio of the corresponding goal. + +##### Target width + +Similar to goal width attribute. +Doubled from goal width for each level of preceding target. + +##### Target min duration sum + +Similar to goal min duration sum attribute. +Geometrically interpolated between +initial target duration and goal min duration sum. + +##### Target trial duration + +When MLRsearch focuses on this target, it measures trials +with this duration. +The value is equal to the minimum of goal final trial duration +and target min duration sum. + +Also, this value is used to classify trial results +as short (if trial duration is shorter than this) or long. + +#### Derived targets + +After receiving the set of search goals, +MLRsearch internally derives a set of search targets. + +The derived targets can be seen as forming a chain, +from initial target to final target. +The chain is linked by a reference from a target to its preceding +(towarsds initial) target. + +The reference may be implemented as 6th attribute od target. + +##### Final target + +The final target is the target where the most of attribute values +are directly copied from the coresponding search goal. +Final target width is the same as goal width, +final target trial duration is the same as goal final trial duration, +and final target min duration sum is the same +as the goal min duration sum. + +The conditional throughput is found when focusing on the final target. +All non-final targets do not directly affect the conditional throughput, +they are there just as an optimization. + +##### Preceding target + +Each target may have a preceding target. +Goal attribute Preceding targets governs how many targets are created +in addition to the final target corresponding to the search goal. + +Any preceding target has double width, meaning one balanced bisection +is needed to reduce preceding target width to the next target width. + +Preceding target min duration sum is exponentially smaller, +aiming for prescribed initial target min duration sum. + +Preceding target trial duration is either its min duration sum, +or the corresponding goal's final trial duration, whichever is smaller. + +As the preceding min duration sum is shorter than the next duration sum, +MLRsearch is able to achieve the preceding target width +sooner (than with the next target min duration sum). + +This way an approximation of the conditional throughput is found, +with the next target needing not as much time to improve the approximation +(compared to not starting with the approximation). + +##### Initial target + +Initial target is a target without any other target preceding it. +Initial target min duration sum is equal to the corresponding goal's +initial trial duration. + +As a consequence, initial target trial duration is equal to its min duration sum. + +#### Trial classification + +Any trial result can be classified according to any target along two axes. + +The two classifications are independent. + +This classification is important for defining the conditional throughput. + +##### Short trial + +If the (measured) trial duration is shorter than +the target trial duration, the trial is called long. + +##### Long trial + +If the (measured) trial duration is not shorter than +the target trial duration, the trial is called long. + +##### Bad trial + +If the (measured) trial loss ratio is larger than the target loss ratio, +the trial is called bad. + +For example, if the target loss ratio is zero, +a trial is bad as soon as one frame was lost. + +##### Good trial + +If the (measured) trial loss ratio is not larger than the target loss ratio, +the trial is called good. + +For example, if the target loss ratio is zero, +a trial is good only when there were no frames lost. + +#### Load stat + +A composite of 8 quantities (see subsections) +The quantites depend on a target and a load, +and are computed from all trials measured at that load so far. + +The MLRsearch output is the conditional througput, +which is a specific quantity based on load. +As MLRsearch may measure multiple trials at the same load, +and those trials may not have the same duration, +we need a way to classify a set of trial results at the same load. + +As the logic is not as straightforward as in other parts +of MLRsearch algorithm, it is best defined using the following +derived quantities. + +Load stat is the composite for one load and one target. +Set of load stats for one load an all targets is commonly called load stats. + +##### Long good duration sum + +Sum of durations of all long good trials +(at this load, according to this target). + +##### Long bad duration sum + +Sum of durations of all long bad trials +(at this load, according to this target). + +##### Short good duration sum + +Sum of durations of all short good trials +(at this load, according to this target). + +##### Short bad duration sum + +Sum of durations of all short bad trials +(at this load, according to this target). + +##### Effective bad duration sum + +One divided by tagret exceed ratio, that plus one. +Short good duration sum divided by that. +Short bad duration sum minus that, or zero if that would be negative. +Long bad duration sum plus that is the effective bad duration sum. + +Effective bad duration sum is the long bad duration sum +plus some fraction of short bad duration sum. +The fraction is between zero and one (both possibly including). + +If there are no short good trials, effective bad duration sum +becomes the duration sum of all bad trials (long or short). + +If an exceed ratio computed from short good duration sum +and short bad duration sum is equal or smaller than the target exceed ratio, +effective bad duration sum is equal to just long bad duration sum. + +Basically, short good trials can only lessen the impact +of short bad trials, while short bad trials directly contribute +(unless lessened). + +A typical example of why a goal needs higher final trial duration +than initial trial duration is when SUT is expected to have large buffers, +so a trial may be too short to see frame losses due to +a buffer becoming full. So a short good trial does not give strong information. +On the other hand, short bad trial is a strong hint SUT would lose many frames +at that load and long duration. +But if there is a mix of short bad and short good trials, +MLRsearch should not cherry-pick only the short bad ones. + +The presented way of computing the effective bad duration sum +aims to be a fair treatment of short good trials. + +If the target exceed ratio is zero, the given definition contains +positive infinty as an intermediate value, but still simplifies +to a finite result (long bad duration sum plus short bad duration sum). + +##### Missing duration sum + +The target min duration sum minus effective bad duration sum +and minus long good duration sum, or zero if that would be negative. + +MLRsearch may need up to this duration sum of additional long trials +before classifing the load. + +##### Optimistic exceed ratio + +The specific quantity based on exceed ratio, where bad duration sum is +the effective bad duration sum, and good duration sum is +the long good duration sum plus the missing duration sum. + +This is the value MLRsearch would compare to target exceed ratio +assuming all of the missing duration sum ends up consisting of long good trials. + +If there was a bad long trial, optimistic exceed ratio +becomes larger than zero. +Additionally, if the target exceed ratio is zero, optimistic exceed ratio +becomes larger than zero even on one short bad trial. + +##### Pessimistic exceed ratio + +The specific quantity based on exceed ratio, where bad duration sum is +the effective bad duration sum plus the missing duration sum, +and good duration sum is the long good duration sum. + +This is the value MLRsearch would compare to target exceed ratio +assuming all of the missing duration sum ends up consisting of bad good trials. + +Note that if the missing duration sum is zero, +optimistic exceed ratio becomes equal to pessimistic exceed ratio. + +This is the role target min duration sum has, +it guarantees the two load exceed ratios eventually become the same. +Otherwise, pessimistic exceed ratio +is always bigger than the optimistic exceed ratio. + +Depending on trial results, the missing duration sum may not be large enough +to change optimistic (or pessimistic) exceed ratio +to move to the other side compared to target exceed ratio. +In that case, MLRsearch does not need to measure more trials +at this load when focusing on this target. + +#### Target bounds + +With respect to a target, some loads may be classified as upper or lower bound, +and some of the bounds are treated as relevant. + +The subsequent parts of MLRsearch rely only on relevant bounds, +without the need to classify other loads. + +##### Upper bound + +A load is classified as an upper bound for a target, +if and only if both optimistic exceed ratio +and pessimstic load exceed ratio are larger than the target exceed ratio. + +During the search, it is possible there is no upper bound, +for example because every measured load still has too high +missing duration sum. + +If the target exceed ratio is zero, and the load has at least one bad trial +(short or long), the load becomes an upper bound. + +##### Lower bound + +A load is classified as a lower bound for a target, +if and only if both optimistic exceed ratio +and pessimstic load exceed ratio are no larger than the target exceed ratio. + +During the search, it is possible there is no lower bound, +for example because every measured load still has too high +missing duration sum. + +If the target exceed ratio is zero, all trials at the load of +a lower bound must be good trials (short or long). + +Note that so far it is possible for a lower bound to be higher +than an upper bound. + +##### Relevant upper bound + +For a target, a load is the relevant upper bound, +if and only if it is an upper bound, and all other upper bounds +are larger (as loads). + +In some cases, the max load when classified as a lower bound +is also effectively treated as the relevant upper bound. +(In that case both relevant bounds are equal.) + +If that happens for a final target at the end of the search, +the controller output may contain max load as the relevant upper bound +(even if the goal exceed ratio was not exceeded), +signalling SUT performs well even at max load. + +If the target exceed ratio is zero, the relevant upper bound +is the smallest load where a bad trial (short or long) has been measured. + +##### Relevant lower bound + +For a target, a load is the relevant lower bound if two conditions hold. +Both optimistic exceed ratio and pessimstic load exceed ratio +are no larger than the target exceed ratio, +and there is no smaller load classified as an upper bound. + +This is a second place where MLRsearch is not symmetric +(the first place was effective bad duration sum). + +While it is not likely for a MLRsearch to find a smaller upper bound +and a larger load satisfying first condition for the lower bound, +it still may happen and MLRsearch has to deal with it. +The second condition makes sure the relevant lower bound +is smaller than the relevant upper bound. + +In some cases, the min load when classified as an upper bound +is also effectively treated as the relevant lower bound. +(In that case both relevant bounds are equal.) + +If that happens for a final target at the end of the search, +the controller output may contain min load as the relevant lower bound +even if the exceed ratio was 'overstepped', +signalizing the SUT does not even reach the minimal required performance. + +The manager has to make sure this is distingushed in report +from cases where min rate is a legitimate conditional throughput +(e.g. the exceed ratio was not overstepped at the min load). + +##### Relevant bounds + +The pair of the relevant lower bound and the relevant upper bound. + +Useful for determining the width of the relevant bounds. +Any of the bounds may be the effective one (max load or min load). + +A goal is achieved (at the end of the search) when the final target's +relevant bounds have width no larger than the goal width. + +#### Candidate selector + +A stateful object (a finite state machine) +focusing on a single target, used to determine next trial input. + +Initialized for a pair of targets: +the current target and its preceding target (if any). + +Private state (not shared with other selectors) consists of mode and flags. +Public state (shared with all selectors) is the actual relevant bounds +for both targets (current and precedinig). + +After accepting a trial result, each selector can nominate +one candidate (or no candidate) for the next trial measurement. + +##### Current target + +This is the target this selector tries to achieve. + +##### Preceding target + +The target (if any) preceding to the current target. + +While this selector does not focus on the preceding target, +the relevant bounds for the preceding target are used as hints +when the current bound does not have enough of its relevant bounds. + +##### Candidate + +The trial input (if any) this selecor nominates. + +The trial duration attribute is always the current target trial duration. +The trial load attribute depends on the selector state. + +Candidates have defined ordering, to simplify finding the winner. +If load differs, the candidate with lower load is preferred. +If load is the same but duration differs, the candidate +with larger duration is preferred. + +##### Selector mode + +During its lifetime, selector proceeds through the following modes. +In order, but some modes may be skipped or revisited. + +Each mode has its own strategy of determining the candidate load (if any). + +###### Waiting + +Not enough relevant bounds (even for the preceding target). +In this mode, the selector abstains from nominating a candidate. + +This selector leaves this mode when preceding target's selector is done. + +###### Halving + +Candidate is in the middle of the relevant bounds of the preceding target. + +If the relevant bounds are narrow enough already, this mode is skipped. +As the preceding target had double width, just one halving load +needs to be measured. + +Selector uses a flag to avoid re-entering this mode +once it finished measuring the halved load. + +###### Upgrading + +This mode activates when one relevant bound for the current target is present +and there is a matching relevant bound of the preceding target +within the current target width. +Candidate is the load of the matching bound from the preceding target. + +At most one bound load is measured, depending on halving outcome. +Private flags are used to avoid upgrading at later times +once selector finished measuring the upgraded load. + +###### Extending + +Refined already but the other relevant bound for the current target +is still missing. +Nominate new candidate according to external search. +Initial target selectors skip all previous modes. + +A private value is used to track the width to be used in next load extension +(increasing geometrically). +For initial target selectors, the starting width may be chosen +based on pre-initial trial results. + +If both relevant bounds are present at the current load, +but the lower bound is far away (compared to tracked width), +the candidate from this mode is preferred (as long as the load +is larger than the candidate load of bisecting mode). + +###### Bisecting + +Both relevant bounds for the current target are available, but they are too far +from each other. Candidate is in the middle. + +Contrary to halving, the candidate load does not need to be at the exact middle. +For example if the width of the current relevant bounds +is three times as large as the target width, +it is advantageous to split the interval in 1:2 ratio +(choosing the lower candidate load), as it can save one bisect. + +###### Done + +Both relevant bounds for the current target are available, +the width is no larger than the target width. +No candidate. + +If a selector reaches the done state, +it is still possible later trials invalidate its relevant lower bound +(by proving a lower load is in fact a new uper bound), +making the selector transition into extending or bisecting mode. + +##### Active selector + +Derived from a common goal, the earliest selector which nominates a candidate +is considered to be the active selector for this goal. +Candidates from other selectors of the same goal are ignored. + +It is quite possible selectors focusing on other goals +have already found a lower bound relevant to multiple targets in a chain. +In that case, we want the most-initial of the target selectors +(not already in done mode) to have the nomination. + +Otherwise (when in extending mode and missun relevant upper bound) +the closer-to-final selectors would nominate candidates +at lower load but at too high duration sum, +preventing some of the time savings. + +##### Winner + +If the candidate previously nominated by a selector was the one +that got measured, the candidate is called a winner. + +A selector observing its previous candidate was a winer +can use simplified logic when determining the mode, +as it knows no other selectors may have changed the relevant loads unexpectedly. + +### Controller output + +The output object the controller returns to the manager +is a mapping assigning each search goal its conditional output (if it exists). + +The controller MAY include more information (if manager accepts it), +for example load stat at relevant bounds. + +There MAY be several ways how to communicate the fact a conditional output +does not exist (e.g. min load is classified as an upper bound). +The manager MUST NOT present min load as a conditional output in that case. + +If max load is a lower bound, it leads to a valid conditional output value. + +#### Conditional throughput + +The conditional throughput is the average of trial forwarding rates +across long good trials measured at the (offered load classified as) +relevant lower bound (for the goal, at the end of the search). +The average is the weighted arithmetic mean, weighted by trial duration. + +If the goal exceed ratio is zero, the definition of the relevant bounds +simplifies significantly. +If additionally the goal loss ratio is zero, +and the goal min duration sum is equal to goal final trial duration, +conditional throughput becomes conditionally compliant with RFC 2544 throughput. +If the goal final trial duration is at least 60 seconds, +the conditional througput becomes unconditionally compliant +with RFC 2544 throughput. + +# Problems + +## Long Test Duration + +Emergence of software DUTs, with frequent software updates and a +number of different packet processing modes and configurations, drives +the requirement of continuous test execution and bringing down the test +execution time. + +In the context of characterising particular DUT's network performance, this +calls for improving the time efficiency of throughput search. +A vanilla bisection (at 60sec trial duration for unconditional [RFC2544] +compliance) is slow, because most trials spend time quite far from the +eventual throughput. + +[RFC2544] does not specify any stopping condition for throughput search, +so users can trade-off between search duration and achieved precision. +But, due to exponential behavior of bisection, small improvement +in search duration needs relatively big sacrifice in the throughput precision. + +## DUT within SUT + +[RFC2285] defines: +- *DUT* as + - The network forwarding device to which stimulus is offered and + response measured [RFC2285] (section 3.1.1). +- *SUT* as + - The collective set of network devices to which stimulus is offered + as a single entity and response measured [RFC2285] (section 3.1.2). + +[RFC2544] specifies a test setup with an external tester stimulating the +networking system, treating it either as a single DUT, or as a system +of devices, an SUT. + +In case of software networking, the SUT consists of a software program +processing packets (device of interest, the DUT), +running on a server hardware and using operating system functions as appropriate, +with server hardware resources shared across all programs +and the operating system. + +DUT is effectively "nested" within SUT. + +Due to a shared multi-tenant nature of SUT, DUT is subject to +interference (noise) coming from the operating system and any other +software running on the same server. Some sources of noise can be +eliminated (e.g. by pinning DUT program threads to specific CPU cores +and isolating those cores to avoid context switching). But some +noise remains after all such reasonable precautions are applied. This +noise does negatively affect DUT's network performance. We refer to it +as an *SUT noise*. + +DUT can also exhibit fluctuating performance itself, e.g. while performing +some "stop the world" 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. We use *noise* as a shorthand covering both *DUT fluctuations* and +genuine SUT noise. + +A simple model of SUT performance consists of a baseline *noiseless performance*, +and an additional noise. The baseline is assumed to be constant (enough). +The noise varies in time, sometimes wildly. The noise can sometimes be negligible, +but frequently it lowers the observed SUT performance in a trial. + +In this model, SUT does not have a single performance value, it has a spectrum. +One end of the spectrum is the noiseless baseline, +the other end is a *noiseful performance*. In practice, trial results +close to the noiseful end of the spectrum happen only rarely. +The worse performance, the more rarely it is seen in a trial. + +Focusing on DUT, the benchmarking effort should aim +at eliminating only the SUT noise from SUT measurement. +But that is not really possible, as there are no realistic enough models +able to distinguish SUT noise from DUT fluctuations. + +However, assuming that a well-constructed SUT has the DUT as its +performance bottleneck, the "DUT noiseless performance" can be defined +as the noiseless end of SUT performance spectrum. (At least for +throughput. For other quantities such as latency there will be an +additive difference.) By this definition, DUT noiseless performance +also minimizes the impact of DUT fluctuations. + +In this document, we reduce the "DUT within SUT" problem to estimating +the noiseless end of SUT performance spectrum from a limited number of +trial results. + +Any improvements to throughput search algorithm, aimed for better +dealing with software networking SUT and DUT setup, should employ +strategies recognizing the presence of SUT noise, and allow discovery of +(proxies for) DUT noiseless performance +at different levels of sensitivity to SUT noise. + +## Repeatability and Comparability + +[RFC2544] does not suggest to repeat throughput search. And from just one +throughput value, it cannot be determined how repeatable that value is. +In practice, poor repeatability is also the main cause of poor +comparability, e.g. different benchmarking teams can test the same SUT +but get different throughput values. + +[RFC2544] throughput requirements (60s trial, no tolerance to single frame loss) +force the search to fluctuate close the noiseful end of SUT performance +spectrum. As that end is affected by rare trials of significantly low +performance, the resulting throughput repeatability is poor. + +The repeatability problem is the problem of defining a search procedure +which reports more stable results +(even if they can no longer be called "throughput" in [RFC2544] sense). +According to baseline (noiseless) and noiseful model, better repeatability +will be at the noiseless end of the spectrum. +Therefore, solutions to the "DUT within 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 "important" tests, 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. + +and then it says: + Since even the loss of one frame in a + data stream can cause significant delays while + waiting for the higher level protocols to time out, + it is useful to know the actual maximum data + rate that the device can support. + +Contrary to that, many benchmarking teams settle with non-zero +(small) loss ratio as the goal for a "throughput rate". + +Motivations are many: modern protocols tolerate frame loss better; +trials nowadays send way more frames within the same duration; +impact of rare noise bursts is smaller as the baseline performance +can compensate somewhat by keeping the loss ratio below the goal; +if SUT noise with "ideal DUT" is known, it can be set as the loss ratio goal. + +Regardless of validity of any and all similar motivations, +support for non-zero loss goals makes any search algorithm more user-friendly. +[RFC2544] throughput is not friendly in this regard. + +Searching for multiple goal loss ratios also helps to describe the SUT +performance better than a single goal result. Repeated wide gap between +zero and non-zero loss conditional throughputs indicates +the noise has a large impact on the overall SUT performance. + +It is easy to modify the vanilla bisection to find a lower bound +for intended load that satisfies a non-zero-loss goal, +but it is not that obvious how to search for multiple goals at once, +hence the support for multiple loss goals remains a problem. + +## Inconsistent Trial Results + +While performing throughput search by executing a sequence of +measurement trials, there is a risk of encountering inconsistencies +between trial results. + +The plain bisection never encounters inconsistent trials. +But [RFC2544] hints about possibility if inconsistent trial results in two places. +The first place is section 24 where full trial durations are required, presumably +because they can be inconsistent with results from shorter trial durations. +The second place is section 26.3 where two successive zero-loss trials +are recommended, presumably because after one zero-loss trial +there can be subsequent inconsistent non-zero-loss trial. + +Examples include: + +- a trial at the same load (same or different trial duration) results + in a different packet loss ratio. +- a trial at higher load (same or different trial duration) results + in a smaller packet loss ratio. + +Any robust throughput search algorithm needs to decide how to continue +the search in presence of such inconsistencies. +Definitions of throughput in [RFC1242] and [RFC2544] are not specific enough +to imply a unique way of handling such inconsistencies. + +Ideally, there will be a definition of a quantity which both generalizes +throughput for non-zero-loss (and other possible repeatibility enhancements), +while being precise enough to force a specific way to resolve trial +inconsistencies. +But until such definition is agreed upon, the correct way to handle +inconsistent trial results remains an open problem. + +# How the problems are addressed + +Configurable loss ratio in MLRsearch search goals are there +in direct support for non-zero-loss conditional throughput. +In practice the conditional throughput results' stability +increases with higher loss ratio goals. + +Multiple trials with noise tolerance enhancement, +as implemented in MLRsearch using non-zero goal exceed ratio value, +also indirectly increases the result stability. +That allows MLRsearch to achieve all the benefits +of Binary Search with Loss Verification, +as recommended in [RFC9004] (section 6.2) +and specified in [TST009] (section 12.3.3). + +The main factor improving the overall search time is the introduction +of preceding targets. Less impactful time savings +are achieved by pre-initial trials, halving mode +and smart splitting in bisecting mode. + +In several places, MLRsearch is "conservative" when handling +(potentially) inconsistent results. This includes the requirement +for the relevant lower bound to be smaller than any upper bound, +the unequal handling of good and bad short trials, +and preference to lower load when choosing the winner among candidates. + +While this does no guarantee good search stability +(goals focusing on higher loads may still invalidate existing bounds +simply by requiring larger min duration sums), +it lowers the change of SUT having an area of poorer performance +below the reported conditional througput loads. +In any case, the definition of conditional throughput +is precise enough to dictate "conservative" handling +of trial inconsistencies. + +# IANA Considerations + +No requests of IANA. + +# Security Considerations + +Benchmarking activities as described in this memo are limited to +technology characterization of a DUT/SUT using controlled stimuli in a +laboratory environment, with dedicated address space and the constraints +specified in the sections above. + +The benchmarking network topology will be an independent test setup and +MUST NOT be connected to devices that may forward the test traffic into +a production network or misroute traffic to the test management network. + +Further, benchmarking is performed on a "black-box" basis, relying +solely on measurements observable external to the DUT/SUT. + +Special capabilities SHOULD NOT exist in the DUT/SUT specifically for +benchmarking purposes. Any implications for network security arising +from the DUT/SUT SHOULD be identical in the lab and in production +networks. + +# Acknowledgements + +Many thanks to Alec Hothan of OPNFV NFVbench project for thorough +review and numerous useful comments and suggestions. + +--- back |