-.. _trending_methodology:
-
Trending Methodology
====================
-Overview
---------
-
-// Below paragraph needs to be updated.
-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.
-
-// Below paragraph needs to be updated.
-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 relies on 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 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.
-
-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.
-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.
-
-// Below paragraph needs to be updated.
-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:
-
-// Below table needs to be updated.
-+-------------------------+---------------------------------+-----------+-------------------------------------------+
-| 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>`_.
+.. toctree::
-.. _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
+ overview
+ trend_analysis
+ trend_presentation
Code Review / csit.git / blobdiff