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-.. _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`_.
-
-TODO: Decide the level of detail for describing group selection.
-
-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.
-
-Metrics
-```````
-
-TODO: Only needed for current trend compliance.
-
-Following statistical metrics are used as performance trend indicators
-over the rolling window of last <N> sets of historical measurement data:
-
-- **TMM** : **Trimmed Moving Median**, median across the data set of <N>
- values excluding the outliers. Used as a trending value and as a
- reference for anomaly detection.
-
-Trend Compliance
-````````````````
-
-TODO: Apply new detection algorithm also 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 trend value, TMM[last], to one from week
-ago, TMM[last - 1week] and to the maximum of trend values over last
-quarter except last week, max(TMM[(last - 3mths)..(last - 1week)]),
-respectively. This results in following trend compliance calculations:
-
-+-------------------------+---------------------------------+-----------+------------------------------------------+
-| Trend Compliance Metric | Trend Change Formula | Value | Reference |
-+=========================+=================================+===========+==========================================+
-| Short-Term Change | (Value - Reference) / Reference | TMM[last] | TMM[last - 1week] |
-+-------------------------+---------------------------------+-----------+------------------------------------------+
-| Long-Term Change | (Value - Reference) / Reference | TMM[last] | max(TMM[(last - 3mths)..(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:
-
- - Outlier - gray circle around MRR value point.
- - Regression - red circle.
- - Progression - green circle.
-
-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 (TODO: Update.):
-
- a) If within the range of (TMA +/- 3*TMSD) => Result = Pass,
- Reason = Normal. (to be updated base on the final Jenkins code).
- b) If below the range => Result = Fail, Reason = Regression.
- c) If above the range => 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