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diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst index 7d7604bee8..cbcfcb50cb 100644 --- a/docs/cpta/methodology/index.rst +++ b/docs/cpta/methodology/index.rst @@ -3,273 +3,12 @@ Trending Methodology ==================== -Overview --------- - -This document describes a high-level design of a system for continuous -performance measuring, trending and change detection for FD.io VPP SW -data plane (and other performance tests run within CSIT sub-project). - -There is a Performance Trending (PT) CSIT module, and a separate -Performance Analysis (PA) module ingesting results from PT and -analysing, detecting and reporting any performance anomalies using -historical data and statistical metrics. PA does also produce -trending dashboard, list of failed tests and graphs with summary and -drill-down views across all specified tests that can be reviewed and -inspected regularly by FD.io developers and users community. - -Performance Tests ------------------ - -Performance trending relies on Maximum Receive Rate (MRR) tests. -MRR tests measure the packet forwarding rate, in multiple trials of set -duration, under the maximum load offered by traffic generator -regardless of packet loss. Maximum load for specified Ethernet frame -size is set to the bi-directional link rate. - -Current parameters for performance trending MRR tests: - -- **Ethernet frame sizes**: 64B (78B for IPv6 tests) for all tests, IMIX for - selected tests (vhost, memif); all quoted sizes include frame CRC, but - exclude per frame transmission overhead of 20B (preamble, inter frame - gap). -- **Maximum load offered**: 10GE and 40GE link (sub-)rates depending on NIC - tested, with the actual packet rate depending on frame size, - transmission overhead and traffic generator NIC forwarding capacity. - - - For 10GE NICs the maximum packet rate load is 2* 14.88 Mpps for 64B, - a 10GE bi-directional link rate. - - For 40GE NICs the maximum packet rate load is 2* 18.75 Mpps for 64B, - a 40GE bi-directional link sub-rate limited by the packet forwarding - capacity of 2-port 40GE NIC model (XL710) used on T-Rex Traffic - Generator. - -- **Trial duration**: 1 sec. -- **Number of trials per test**: 10. -- **Test execution frequency**: twice a day, every 12 hrs (02:00, - 14:00 UTC). - -Note: MRR tests should be reporting bi-directional link rate (or NIC -rate, if lower) if tested VPP configuration can handle the packet rate -higher than bi-directional link rate, e.g. large packet tests and/or -multi-core tests. In other words MRR = min(VPP rate, bi-dir link rate, -NIC rate). - -Trend Analysis --------------- - -All measured performance trend data is treated as time-series data that -can be modelled as concatenation of groups, each group modelled -using normal distribution. While sometimes the samples within a group -are far from being distributed normally, currently we do not have a -better tractable model. - -Here, "sample" should be the result of single trial measurement, -with group boundaries set only at test run granularity. -But in order to avoid detecting causes unrelated to VPP performance, -the default presentation (without /new/ in URL) -takes average of all trials within the run as the sample. -Effectively, this acts as a single trial with aggregate duration. - -Performance graphs always show the run average (not all trial results). - -The group boundaries are selected based on `Minimum Description Length`_. - -Minimum Description Length --------------------------- - -`Minimum Description Length`_ (MDL) is a particular formalization -of `Occam's razor`_ principle. - -The general formulation mandates to evaluate a large set of models, -but for anomaly detection purposes, it is useful to consider -a smaller set of models, so that scoring and comparing them is easier. - -For each candidate model, the data should be compressed losslessly, -which includes model definitions, encoded model parameters, -and the raw data encoded based on probabilities computed by the model. -The model resulting in shortest compressed message is the "the" correct model. - -For our model set (groups of normally distributed samples), -we need to encode group length (which penalizes too many groups), -group average (more on that later), group stdev and then all the samples. - -Luckily, the "all the samples" part turns out to be quite easy to compute. -If sample values are considered as coordinates in (multi-dimensional) -Euclidean space, fixing stdev means the point with allowed coordinates -lays on a sphere. Fixing average intersects the sphere with a (hyper)-plane, -and Gaussian probability density on the resulting sphere is constant. -So the only contribution is the "area" of the sphere, which only depends -on the number of samples and stdev. - -A somehow ambiguous part is in choosing which encoding -is used for group size, average and stdev. -Different encodings cause different biases to large or small values. -In our implementation we have chosen probability density -corresponding to uniform distribution (from zero to maximal sample value) -for stdev and average of the first group, -but for averages of subsequent groups we have chosen a distribution -which disourages delimiting groups with averages close together. - -Our implementation assumes that measurement precision is 1.0 pps. -Thus it is slightly wrong for trial durations other than 1.0 seconds. -Also, all the calculations assume 1.0 pps is totally negligible, -compared to stdev value. - -The group selection algorithm currently has no parameters, -all the aforementioned encodings and handling of precision is hardcoded. -In principle, every group selection is examined, and the one encodable -with least amount of bits is selected. -As the bit amount for a selection is just sum of bits for every group, -finding the best selection takes number of comparisons -quadratically increasing with the size of data, -the overall time complexity being probably cubic. - -The resulting group distribution looks good -if samples are distributed normally enough within a group. -But for obviously different distributions (for example `bimodal distribution`_) -the groups tend to focus on less relevant factors (such as "outlier" density). - -Anomaly Detection -````````````````` - -Once the trend data is divided into groups, each group has its population average. -The start of the following group is marked as a regression (or progression) -if the new group's average is lower (higher) then the previous group's. - -In the text below, "average at time <t>", shorthand "AVG[t]" -means "the group average of the group the sample at time <t> belongs to". - -Trend Compliance -```````````````` - -Trend compliance metrics are targeted to provide an indication of trend -changes over a short-term (i.e. weekly) and a long-term (i.e. -quarterly), comparing the last group average AVG[last], to the one from week -ago, AVG[last - 1week] and to the maximum of trend values over last -quarter except last week, max(AVG[last - 3mths]..ANV[last - 1week]), -respectively. This results in following trend compliance calculations: - -+-------------------------+---------------------------------+-----------+-------------------------------------------+ -| Trend Compliance Metric | Trend Change Formula | Value | Reference | -+=========================+=================================+===========+===========================================+ -| Short-Term Change | (Value - Reference) / Reference | AVG[last] | AVG[last - 1week] | -+-------------------------+---------------------------------+-----------+-------------------------------------------+ -| Long-Term Change | (Value - Reference) / Reference | AVG[last] | max(AVG[last - 3mths]..AVG[last - 1week]) | -+-------------------------+---------------------------------+-----------+-------------------------------------------+ - -Trend Presentation ------------------- - -Performance Dashboard -````````````````````` - -Dashboard tables list a summary of per test-case VPP MRR performance -trend and trend compliance metrics and detected number of anomalies. - -Separate tables are generated for each testbed and each tested number of -physical cores for VPP workers (1c, 2c, 4c). Test case names are linked to -respective trending graphs for ease of navigation through the test data. - -Failed tests -```````````` - -The Failed tests tables list the tests which failed over the specified seven- -day period together with the number of fails over the period and last failure -details - Time, VPP-Build-Id and CSIT-Job-Build-Id. - -Separate tables are generated for each testbed. Test case names are linked to -respective trending graphs for ease of navigation through the test data. - -Trendline Graphs -```````````````` - -Trendline graphs show measured per run averages of MRR values, -group average values, and detected anomalies. -The graphs are constructed as follows: - -- X-axis represents the date in the format MMDD. -- Y-axis represents run-average MRR value in Mpps. -- Markers to indicate anomaly classification: - - - Regression - red circle. - - Progression - green circle. - -- The line shows average MRR value of each group. - -In addition the graphs show dynamic labels while hovering over graph -data points, presenting the CSIT build date, measured MRR value, VPP -reference, trend job build ID and the LF testbed ID. - -Jenkins Jobs ------------- - -Performance Trending (PT) -````````````````````````` - -CSIT PT runs regular performance test jobs measuring and collecting MRR -data per test case. PT is designed as follows: - -1. PT job triggers: - - a) Periodic e.g. twice a day. - b) On-demand gerrit triggered. - -2. Measurements and data calculations per test case: - - a) Max Received Rate (MRR) - for each trial measurement, - send packets at link rate for trial duration, - count total received packets, divide by trial duration. - -3. Archive MRR values per test case. -4. Archive all counters collected at MRR. - -Performance Analysis (PA) -````````````````````````` - -CSIT PA runs performance analysis -including anomaly detection as described above. -PA is defined as follows: - -1. PA job triggers: - - a) By PT jobs at their completion. - b) On-demand gerrit triggered. - -2. Download and parse archived historical data and the new data: - - a) Download RF output.xml files from latest PT job and compressed - archived data from nexus. - b) Parse out the data filtering test cases listed in PA specification - (part of CSIT PAL specification file). - -3. Re-calculate new groups and their averages. - -4. Evaluate new test data: - - a) If the existing group is prolonged => Result = Pass, - Reason = Normal. - b) If a new group is detected with lower average => - Result = Fail, Reason = Regression. - c) If a new group is detected with higher average => - Result = Pass, Reason = Progression. - -5. Generate and publish results - - a) Relay evaluation result to job result. - b) Generate a new set of trend summary dashboard, list of failed - tests and graphs. - c) Publish trend dashboard and graphs in html format on - https://docs.fd.io/. - d) Generate an alerting email. This email is sent by Jenkins to - csit-report@lists.fd.io - -Testbed HW configuration ------------------------- - -The testbed HW configuration is described on -`this FD.IO wiki page <https://wiki.fd.io/view/CSIT/CSIT_LF_testbed#FD.IO_CSIT_testbed_-_Server_HW_Configuration>`_. - -.. _Minimum Description Length: https://en.wikipedia.org/wiki/Minimum_description_length -.. _Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor -.. _bimodal distribution: https://en.wikipedia.org/wiki/Bimodal_distribution +.. toctree:: + + overview + performance_tests + trend_analysis + trend_presentation + jenkins_jobs + testbed_hw_configuration + perpatch_performance_tests |