-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. It builds upon the existing FD.io CSIT framework with
+extensions to its throughput testing methodology, CSIT data analytics
+engine (PAL – Presentation-and-Analytics-Layer) and associated Jenkins
+jobs definitions.
+
+Proposed design replaces existing CSIT performance trending jobs and
+tests with new Performance Trending (PT) CSIT module and separate
+Performance Analysis (PA) module ingesting results from PT and
+analysing, detecting and reporting any performance anomalies using
+historical trending data and statistical metrics. PA does also produce
+trending dashboard 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 is currently relying on the Maximum Receive Rate
+(MRR) tests. MRR tests measure the packet forwarding rate under the
+maximum load offered by traffic generator over a set trial duration,
+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 TG 40GE NIC used,
+ XL710.
+
+- **Trial duration**: 10sec.
+- **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, we do not have a better tractable model.
+
+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 usefuls 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.
+Diferent 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 deliminating groups with averages close together.
+
+One part of our implementation which is not precise enough
+is handling of measurement precision.
+The minimal difference in MRR values is currently 0.1 pps
+(the difference of one packet over 10 second trial),
+but the code assumes the precision is 1.0.
+Also, all the calculations assume 1.0 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.
+
+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 tested VPP worker-thread-core
+combinations (1t1c, 2t2c, 4t4c). Test case names are linked to
+respective trending graphs for ease of navigation thru the test data.
+
+Trendline Graphs
+````````````````
+
+Trendline graphs show per test case measured MRR throughput values with
+associated gruop averages. The graphs are constructed as follows:
+
+- X-axis represents performance trend job build Id (csit-vpp-perf-mrr-
+ daily-master-build).
+- Y-axis represents MRR throughput in Mpps.
+- Markers to indicate anomaly classification:
+
+ - Regression - red circle.
+ - Progression - green circle.
+
+- The line shows average of each group.
+
+In addition the graphs show dynamic labels while hovering over graph
+data points, representing (trend job build Id, MRR value) and the actual
+vpp build number (b<XXX>) tested.
+
+
+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. daily.
+ b) On-demand gerrit triggered.
+
+2. Measurements and data calculations per test case:
+
+ a) Max Received Rate (MRR) - send packets at link rate over a trial
+ period, count total received packets, divide by trial period.
+
+3. Archive MRR per test case.
+4. Archive all counters collected at MRR.
+
+Performance Analysis (PA)
+`````````````````````````
+
+CSIT PA runs performance analysis including trendline calculation, trend
+compliance and anomaly detection using specified trend analysis metrics
+over the rolling window of last <N> sets of historical measurement data.
+PA is defined as follows:
+
+1. PA job triggers:
+
+ a) By PT job at its 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.
+ 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. (to be updated base on the final Jenkins code).
+ 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. (to be updated base on the
+ final Jenkins code).
+ b) Generate a new set of trend summary dashboard and graphs.
+ c) Publish trend dashboard and graphs in html format on
+ https://docs.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