-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 <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
Code Review / csit.git / blobdiff