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diff --git a/docs/cpta/introduction/index.rst b/docs/cpta/introduction/index.rst index 944a56e383..5d31b33328 100644 --- a/docs/cpta/introduction/index.rst +++ b/docs/cpta/introduction/index.rst @@ -1,181 +1,8 @@ -Introduction -============ - -Purpose -------- - -With increasing number of features and code changes in the FD.io VPP data plane -codebase, it is increasingly difficult to measure and detect VPP data plane -performance changes. Similarly, once degradation is detected, it is getting -harder to bisect the source code in search of the Bad code change or addition. -The problem is further escalated by a large combination of compute platforms -that VPP is running and used on, including Intel Xeon, Intel Atom, ARM Aarch64. - -Existing FD.io CSIT continuous performance trending test jobs help, but they -rely on human factors for anomaly detection, and as such are error prone and -unreliable, as the volume of data generated by these jobs is growing -exponentially. - -Proposed solution is to eliminate human factor and fully automate performance -trending, regression and progression detection, as well as bisecting. - -This document describes a high-level design of a system for continuous -measuring, trending and performance change detection for FD.io VPP SW data -plane. It builds upon the existing CSIT framework with extensions to its -throughput testing methodology, CSIT data analytics engine -(PAL – Presentation-and-Analytics-Layer) and associated Jenkins jobs -definitions. - -Continuous Performance Trending and Analysis --------------------------------------------- - -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 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. - -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 average and -deviations from average are used for detecting performance change anomalies -following the three-sigma rule of thumb (a.k.a. 68-95-99.7 rule). - -Analysis Metrics -```````````````` - -Following statistical metrics are proposed as performance trend indicators over -the rolling window of last <N> sets of historical measurement data: - - #. Quartiles Q1, Q2, Q3 – three points dividing a ranked set of data set - into four equal parts, Q2 is the median of the data. - #. Inter Quartile Range IQR=Q3-Q1 – measure of variability, used here to - eliminate outliers. - #. Outliers – extreme values that are at least 1.5*IQR below Q1, or at - least 1.5*IQR above Q3. - #. Trimmed Moving Average (TMA) – average across the data set of the rolling - window of <N> values without the outliers. Used here to calculate TMSD. - #. Trimmed Moving Standard Deviation (TMSD) – standard deviation over the - data set of the rolling window of <N> values without the outliers, - requires calculating TMA. Used here for anomaly detection. - #. Moving Median (MM) - median across the data set of the rolling window of - <N> values with all data points, including the outliers. Used here for - anomaly detection. - -Anomaly Detection -````````````````` - -Based on the assumption that all performance measurements can be modelled using -normal distribution, a three-sigma rule of thumb is proposed as the main -criteria for anomaly detection. - -Three-sigma rule of thumb, aka 68–95–99.7 rule, is a shorthand used to capture -the percentage of values that lie within a band around the average (mean) in a -normal distribution within a width of two, four and six standard deviations. -More accurately 68.27%, 95.45% and 99.73% of the result values should lie within -one, two or three standard deviations of the mean, see figure below. - -To verify compliance of test result with value X against defined trend analysis -metric and detect anomalies, three simple evaluation criteria are proposed: - -:: - - Test Result Evaluation Reported Result Reported Reason Trending Graph Markers - ========================================================================================== - Normal Pass Normal Part of plot line - Regression Fail Regression Red circle - Progression Pass Progression Green circle - -Jenkins job cumulative results: - - #. Pass - if all detection results are Pass or Warning. - #. Fail - if any detection result is Fail. - -Performance Trending (PT) -````````````````````````` - -CSIT PT runs regular performance test jobs finding MRR, PDR and NDR per test -cases. PT is designed as follows: - - #. PT job triggers: - - #. Periodic e.g. daily. - #. On-demand gerrit triggered. - #. Other periodic TBD. - - #. Measurements and calculations per test case: - - #. MRR Max Received Rate - - #. Measured: Unlimited tolerance of packet loss. - #. Send packets at link rate, count total received packets, divide - by test trial period. - - #. Optimized binary search bounds for PDR and NDR tests: - - #. Calculated: High and low bounds for binary search based on MRR - and pre-defined Packet Loss Ratio (PLR). - #. HighBound=MRR, LowBound=to-be-determined. - #. PLR – acceptable loss ratio for PDR tests, currently set to 0.5% - for all performance tests. - - #. PDR and NDR: - - #. Run binary search within the calculated bounds, find PDR and NDR. - #. Measured: PDR Partial Drop Rate – limited non-zero tolerance of - packet loss. - #. Measured: NDR Non Drop Rate - zero packet loss. - - #. Archive MRR, PDR and NDR per test case. - #. Archive counters collected at MRR, PDR and NDR. - -Performance Analysis (PA) -````````````````````````` - -CSIT PA runs performance analysis, change detection and trending using specified -trend analysis metrics over the rolling window of last <N> sets of historical -measurement data. PA is defined as follows: - - #. PA job triggers: - - #. By PT job at its completion. - #. Manually from Jenkins UI. - - #. Download and parse archived historical data and the new data: - - #. New data from latest PT job is evaluated against the rolling window - of <N> sets of historical data. - #. Download RF output.xml files and compressed archived data. - #. Parse out the data filtering test cases listed in PA specification - (part of CSIT PAL specification file). - - #. Calculate trend metrics for the rolling window of <N> sets of historical data: - - #. Calculate quartiles Q1, Q2, Q3. - #. Trim outliers using IQR. - #. Calculate TMA and TMSD. - #. Calculate normal trending range per test case based on TMA and TMSD. - - #. Evaluate new test data against trend metrics: - - #. If within the range of (TMA +/- 3*TMSD) => Result = Pass, - Reason = Normal. - #. If below the range => Result = Fail, Reason = Regression. - #. If above the range => Result = Pass, Reason = Progression. - - #. Generate and publish results - - #. Relay evaluation result to job result. - #. Generate a new set of trend analysis summary graphs and drill-down - graphs. - - #. Summary graphs to include measured values with Normal, - Progression and Regression markers. MM shown in the background if - possible. - #. Drill-down graphs to include MM, TMA and TMSD. - - #. Publish trend analysis graphs in html format. +VPP Performance Trending +======================== + +This auto-generated document contains VPP performance trending graphs and data. +It is generated using CSIT continuous trending test and analysis jobs and is +updated daily. More detail is available on +`CSIT Performance Trending and Analysis <https://wiki.fd.io/view/CSIT/PerformanceTrendingAnalysis>`_ +wiki page. |