-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 using normal distribution. After trimming the outliers,
+the median and deviations from median are used for detecting performance
+change anomalies following the three-sigma rule of thumb (a.k.a.
+68-95-99.7 rule).
+
+Metrics
+````````````````
+
+Following statistical metrics are used as performance trend indicators
+over the rolling window of last <N> sets of historical measurement data:
+
+- **Q1**, **Q2**, **Q3** : **Quartiles**, three points dividing a ranked
+ data set of <N> values into four equal parts, Q2 is the median of the
+ data.
+- **IQR** = Q3 - Q1 : **Inter Quartile Range**, measure of variability,
+ used here to calculate and eliminate outliers.
+- **Outliers** : extreme values that are at least (1.5 * IQR) below Q1.
+
+ - Note: extreme values that are at least (1.5 * IQR) above Q3 are not
+ considered outliers, and are likely to be classified as
+ progressions.
+
+- **TMA** : **Trimmed Moving Average**, average across the data set of
+ <N> values without the outliers. Used here to calculate TMSD.
+- **TMSD** : **Trimmed Moving Standard Deviation**, standard deviation
+ over the data set of <N> values without the outliers, requires
+ calculating TMA. Used for anomaly detection.
+- **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.
+
+Outlier Detection
+`````````````````
+
+Outlier evaluation of test result of value *X* follows the definition
+from previous section:
+
++----------------------------+----------------------+
+| Outlier Evaluation Formula | Evaluation Result |
++============================+======================+
+| X < (Q1 - 1.5 * IQR) | Outlier |
++----------------------------+----------------------+
+| X >= (Q1 - 1.5 * IQR) | Valid (For Trending) |
++----------------------------+----------------------+
+
+Anomaly Detection
+`````````````````
+
+To verify compliance of test result of valid value <X> against defined
+trend metrics and detect anomalies, three simple evaluation formulas are
+used:
+
++-------------------------------------------+-----------------------------+-------------------+
+| Anomaly Evaluation Formula | Compliance Confidence Level | Evaluation Result |
++===========================================+=============================+===================+
+| (TMM - 3 * TMSD) <= X <= (TMM + 3 * TMSD) | 99.73% | Normal |
++-------------------------------------------+-----------------------------+-------------------+
+| X < (TMM - 3 * TMSD) | Anomaly | Regression |
++-------------------------------------------+-----------------------------+-------------------+
+| X > (TMM + 3 * TMSD) | Anomaly | Progression |
++-------------------------------------------+-----------------------------+-------------------+
+
+TMM is used for the central trend reference point instead of TMA as it
+is more robust to anomalies.
+
+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 trendlines. 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).
+ c) Evalute new data from latest PT job against the rolling window of
+ <N> sets of historical data for trendline calculation, anomaly
+ detection and short-term trend compliance. And against long-term
+ trendline metrics for long-term trend compliance.
+
+3. Calculate trend metrics for the rolling window of <N> sets of
+ historical data:
+
+ a) Calculate quartiles Q1, Q2, Q3.
+ b) Trim outliers using IQR.
+ c) Calculate TMA and TMSD.
+ d) Calculate normal trending range per test case based on TMM and
+ TMSD.
+
+4. Evaluate new test data against trend metrics:
+
+ 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/.
Code Review / csit.git / blobdiff