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diff --git a/docs/report/introduction/methodology.rst b/docs/report/introduction/methodology.rst index da8f859d8d..4faca6824c 100644 --- a/docs/report/introduction/methodology.rst +++ b/docs/report/introduction/methodology.rst @@ -23,3 +23,4 @@ Test Methodology methodology_ipsec_on_intel_qat methodology_trex_traffic_generator methodology_http_tcp_with_wrk_tool + methodology_plrsearch diff --git a/docs/report/introduction/methodology_plrsearch.rst b/docs/report/introduction/methodology_plrsearch.rst new file mode 100644 index 0000000000..b21fad3677 --- /dev/null +++ b/docs/report/introduction/methodology_plrsearch.rst @@ -0,0 +1,357 @@ +.. _plrsearch_algorithm: + +PLRsearch +^^^^^^^^^ + +Abstract algorithm +~~~~~~~~~~~~~~~~~~ + +.. TODO: Refer to packet forwarding terminology, such as "offered load" and + "loss ratio". + +Eventually, a better description of the abstract search algorithm +will appear at this IETF standard: `plrsearch draft`_. + +Motivation +---------- + +Network providers are interested in throughput a device can sustain. + +`RFC 2544`_ assumes loss ratio is given by a deterministic function of +offered load. But NFV software devices are not deterministic (enough). +This leads for deterministic algorithms (such as MLRsearch with single +trial) to return results, which when repeated show relatively high +standard deviation, thus making it harder to tell what "the throughput" +actually is. + +We need another algorithm, which takes this indeterminism into account. + +Model +----- + +Each algorithm searches for an answer to a precisely formulated +question. When the question involves indeterministic systems, it has to +specify probabilities (or prior distributions) which are tied to a +specific probabilistic model. Different models will have different +number (and meaning) of parameters. Complicated (but more realistic) +models have many parameters, and the math involved can be very +convoluted. It is better to start with simpler probabilistic model, and +only change it when the output of the simpler algorithm is not stable or +useful enough. + +This document is focused on algorithms related to packet loss count +only. No latency (or other information) is taken into account. For +simplicity, only one type of measurement is considered: dynamically +computed offered load, constant within trial measurement of +predetermined trial duration. + +The main idea of the search apgorithm is to iterate trial measurements, +using `Bayesian inference`_ to compute both the current estimate +of "the throughput" and the next offered load to measure at. +The computations are done in parallel with the trial measurements. + +The following algorithm makes an assumption that packet traffic +generator detects duplicate packets on receive detection, and reports +this as an error. + +Poisson distribution +-------------------- + +For given offered load, number of packets lost during trial measurement +is assumed to come from `Poisson distribution`_, +each trial is assumed to be independent, and the (unknown) Poisson parameter +(average number of packets lost per second) is assumed to be +constant across trials. + +When comparing different offered loads, the average loss per second is +assumed to increase, but the (deterministic) function from offered load +into average loss rate is otherwise unknown. This is called "loss function". + +Given a target loss ratio (configurable), there is an unknown offered load +when the average is exactly that. We call that the "critical load". +If critical load seems higher than maximum offerable load, we should use +the maximum offerable load to make search output more conservative. + +Side note: `Binomial distribution`_ is a better fit compared to Poisson +distribution (acknowledging that the number of packets lost cannot be +higher than the number of packets offered), but the difference tends to +be relevant in loads far above the critical region, so using Poisson +distribution helps the algorithm focus on critical region better. + +Of course, there are great many increasing functions (as candidates +for loss function). The offered load has to be chosen for each trial, +and the computed posterior distribution of critical load +changes with each trial result. + +To make the space of possible functions more tractable, some other +simplifying assumptions are needed. As the algorithm will be examining +(also) loads close to the critical load, linear approximation to the +loss function in the critical region is important. +But as the search algorithm needs to evaluate the function also far +away from the critical region, the approximate function has to be +well-behaved for every positive offered load, specifically it cannot predict +non-positive packet loss rate. + +Within this document, "fitting function" is the name for such a well-behaved +function, which approximates the unknown loss function in the critical region. + +Results from trials far from the critical region are likely to affect +the critical rate estimate negatively, as the fitting function does not +need to be a good approximation there. Discarding some results, +or "suppressing" their impact with ad-hoc methods (other than +using Poisson distribution instead of binomial) is not used, as such +methods tend to make the overall search unstable. We rely on most of +measurements being done (eventually) within the critical region, and +overweighting far-off measurements (eventually) for well-behaved fitting +functions. + +Speaking about new trials, each next trial will be done at offered load +equal to the current average of the critical load. +Alternative methods for selecting offered load might be used, +in an attempt to speed up convergence, but such methods tend to be +scpecific for a particular system under tests. + +Fitting function coefficients distribution +------------------------------------------ + +To accomodate systems with different behaviours, the fitting function is +expected to have few numeric parameters affecting its shape (mainly +affecting the linear approximation in the critical region). + +The general search algorithm can use whatever increasing fitting +function, some specific functions can described later. + +It is up to implementer to chose a fitting function and prior +distribution of its parameters. The rest of this document assumes each +parameter is independently and uniformly distributed over a common +interval. Implementers are to add non-linear transformations into their +fitting functions if their prior is different. + +Exit condition for the search is either critical load stdev +becoming small enough, or overal search time becoming long enough. + +The algorithm should report both avg and stdev for critical load. If the +reported averages follow a trend (without reaching equilibrium), avg and +stdev should refer to the equilibrium estimates based on the trend, not +to immediate posterior values. + +Integration +----------- + +The posterior distributions for fitting function parameters will not be +integrable in general. + +The search algorithm utilises the fact that trial measurement takes some +time, so this time can be used for numeric integration (using suitable +method, such as Monte Carlo) to achieve sufficient precision. + +Optimizations +------------- + +After enough trials, the posterior distribution will be concentrated in +a narrow area of parameter space. The integration method should take +advantage of that. + +Even in the concentrated area, the likelihood can be quite small, so the +integration algorithm should track the logarithm of the likelihood, and +also avoid underflow errors by other means. + +FD.io CSIT Implementation Specifics +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The search receives min_rate and max_rate values, to avoid measurements +at offered loads not supporeted by the traffic generator. + +The implemented tests cases use bidirectional traffic. +The algorithm stores each rate as bidirectional rate (internally, +the algorithm is agnostic to flows and directions, +it only cares about overall counts of packets sent and packets lost), +but debug output from traffic generator lists unidirectional values. + +Measurement delay +----------------- + +In a sample implemenation in FD.io CSIT project, there is roughly 0.5 +second delay between trials due to restrictons imposed by packet traffic +generator in use (T-Rex). + +As measurements results come in, posterior distribution computation takes +more time (per sample), although there is a considerable constant part +(mostly for inverting the fitting functions). + +Also, the integrator needs a fair amount of samples to reach the region +the posterior distribution is concentrated at. + +And of course, speed of the integrator depends on computing power +of the CPU the algorithm is able to use. + +All those timing related effects are addressed by arithmetically increasing +trial durations with configurable coefficients +(currently 10.2 seconds for the first trial, +each subsequent trial being 0.2 second longer). + +Rounding errors and underflows +------------------------------ + +In order to avoid them, the current implementation tracks natural logarithm +(instead of the original quantity) for any quantity which is never negative. +Logarithm of zero is minus infinity (not supported by Python), +so special value "None" is used instead. +Specific functions for frequent operations +(such as "logarithm of sum of exponentials") +are defined to handle None correctly. + +Fitting functions +----------------- + +Current implementation uses two fitting functions. +In general, their estimates for critical rate differ, +which adds a simple source of systematic error, +on top of randomness error reported by integrator. +Otherwise the reported stdev of critical rate estimate +is unrealistically low. + +Both functions are not only increasing, but convex +(meaning the rate of increase is also increasing). + +As `primitive function`_ to any positive function is an increasing function, +and primitive function to any increasing function is convex function; +both fitting functions were constructed as double primitive function +to a positive function (even though the intermediate increasing function +is easier to describe). + +As not any function is integrable, some more realistic functions +(especially with respect to behavior at very small offered loads) +are not easily available. + +Both fitting functions have a "central point" and a "spread", +varied by simply shifting and scaling (in x-axis, the offered load direction) +the function to be doubly integrated. +Scaling in y-axis (the loss rate direction) is fixed by the requirement of +transfer rate staying nearly constant in very high offered loads. + +In both fitting functions (as they are a double primitive function +to a symmetric function), the "central point" turns out +to be equal to the aforementioned limiting transfer rate, +so the fitting function parameter is named "mrr", +the same quantity our Maximum Receive Rate tests are designed to measure. + +Both fitting functions return logarithm of loss rate, +to avoid rounding errors and underflows. +Parameters and offered load are not given as logarithms, +as they are not expected to be extreme, +and the formulas are simpler that way. + +Both fitting functions have several mathematically equivalent formulas, +each can lead to an overflow or underflow in different places. +Overflows can be eliminated by using different exact formulas +for different argument ranges. +Underflows can be avoided by using approximate formulas +in affected argument ranges, such ranges have their own formulas to compute. +At the end, both fitting function implementations +contain multiple "if" branches, discontinuities are a possibility +at range boundaries. + +Offered load for next trial measurement is the average of critical rate estimate. +During each measurement, two estimates are computed, +even though only one (in alternating order) is used for next offered load. + +Stretch function +________________ + +The original function (before applying logarithm) is primitive function +to `logistic function`_. +The name "stretch" is used for related function +in context of neural networks with sigmoid activation function. + +Erf function +____________ + +The original function is double primitive function to `Gaussian function`_. +The name "erf" comes from error function, the first primitive to Gaussian. + +Prior distributions +------------------- + +The numeric integrator expects all the parameters to be distributed +(independently and) uniformly on an interval (-1, 1). + +As both "mrr" and "spread" parameters are positive and not not dimensionless, +a transformation is needed. Dimentionality is inherited from max_rate value. + +The "mrr" parameter follows a `Lomax distribution`_ +with alpha equal to one, but shifted so that mrr is always greater than 1 +packet per second. + +The "stretch" parameter is generated simply as the "mrr" value +raised to a random power between zero and one; +thus it follows a `reciprocal distribution`_. + +Integrator +---------- + +After few measurements, the posterior distribution of fitting function +arguments gets quite concentrated into a small area. +The integrator is using `Monte Carlo`_ with `importance sampling`_ +where the biased distribution is `bivariate Gaussian`_ distribution, +with deliberately larger variance. +If the generated sample falls outside (-1, 1) interval, +another sample is generated. + +The center and the variance for the biased distribution has three sources. +First is a prior information. After enough samples are generated, +the biased distribution is constructed from a mixture of two sources. +Top 12 most weight samples, and all samples (the mix ratio is computed +from the relative weights of the two populations). +When integration (run along a particular measurement) is finished, +the mixture bias distribution is used as the prior information +for the next integration. + +This combination showed the best behavior, as the integrator usually follows +two phases. First phase (where the top 12 samples are dominating) +is mainly important for locating the new area the posterior distribution +is concentrated at. The second phase (dominated by whole sample population) +is actually relevant for the critical rate estimation. + +Caveats +------- + +Current implementation does not constrict the critical rate +(as computed for every sample) to the min_rate, max_rate interval. + +Earlier implementations were targeting loss rate (as opposed to loss ratio). +The chosen fitting functions do not even allow arbitrarily low loss ratios, +especially if the "spread" value is high enough (relative to "mrr" value). +Internal loss rate target is computed from given loss ratio +using the current trial offered load, which increases search instability +if measurements with surprisingly high loss count appear. + +As high loss count measurements add many bits of information, +they need a large amount of small loss count measurements to balance them, +making the algorithm converge quite slowly. + +Some systems evidently do not follow the assumption of repeated measurements +having the same average loss rate (when offered load is the same). +The idea of estimating the trend is not implemented at all, +as the observed trends have varied characteristics. + +Probably, using a more realistic fitting functions +will give better estimates than trend analysis. + +.. TODO: Add a 1901 result section when results are available. + +.. TODO: Add a graph of time evolution when 1901 run is available. + +.. _plrsearch draft: https://tools.ietf.org/html/draft-vpolak-bmwg-plrsearch-00 +.. _RFC 2544: https://tools.ietf.org/html/rfc2544 +.. _Bayesian inference: https://en.wikipedia.org/wiki/Bayesian_statistics +.. _Poisson distribution: https://en.wikipedia.org/wiki/Poisson_distribution +.. _Binomial distribution: https://en.wikipedia.org/wiki/Binomial_distribution +.. _primitive function: https://en.wikipedia.org/wiki/Antiderivative +.. _logistic function: https://en.wikipedia.org/wiki/Logistic_function +.. _Gaussian function: https://en.wikipedia.org/wiki/Gaussian_function +.. _Lomax distribution: https://en.wikipedia.org/wiki/Lomax_distribution +.. _reciprocal distribution: https://en.wikipedia.org/wiki/Reciprocal_distribution +.. _Monte Carlo: https://en.wikipedia.org/wiki/Monte_Carlo_integration +.. _importance sampling: https://en.wikipedia.org/wiki/Importance_sampling +.. _bivariate Gaussian: https://en.wikipedia.org/wiki/Multivariate_normal_distribution |