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diff --git a/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md b/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md deleted file mode 100644 index 4db8506131..0000000000 --- a/docs/ietf/draft-ietf-bmwg-mlrsearch-04.md +++ /dev/null @@ -1,1479 +0,0 @@ ---- -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 |