X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;ds=sidebyside;f=docs%2Fcpta%2Fmethodology%2Findex.rst;h=7d7604bee8ab44b31a9aeb1a60e1daa00381824f;hb=1aeacc2c1efb0ce39c2d0789ec04a694a0943ba7;hp=a218f73d6033815d1787abf313ab18dc1120eeed;hpb=6e1a15acc7bd3685d71f9d34238181c681dcd4c0;p=csit.git diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst index a218f73d60..7d7604bee8 100644 --- a/docs/cpta/methodology/index.rst +++ b/docs/cpta/methodology/index.rst @@ -1,3 +1,275 @@ -Methodology -=========== +.. _trending_methodology: +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 ", shorthand "AVG[t]" +means "the group average of the group the sample at time 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 `_. + +.. _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