X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=docs%2Fcpta%2Fintroduction%2Findex.rst;h=df47dc5cd99047acd2bf57b41a5a8c5e24485a0b;hp=944a56e3835095ece18424069bfbe2a8bc12c81f;hb=0f662ea0defa9b30fa7a7d9256857fce92d20a6e;hpb=6ef96e8c0a95dc9ccfaf51fe51b60b7934ed6e89 diff --git a/docs/cpta/introduction/index.rst b/docs/cpta/introduction/index.rst index 944a56e383..df47dc5cd9 100644 --- a/docs/cpta/introduction/index.rst +++ b/docs/cpta/introduction/index.rst @@ -1,181 +1,47 @@ -Introduction -============ +VPP MRR Performance Dashboard +============================= -Purpose -------- +Description +----------- -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. +Dashboard tables list a summary of per test-case VPP MRR performance +trend and trend compliance metrics, and detected number of anomalies. +Data samples come from the CSIT VPP trending MRR jobs executed twice a +day, every 12 hrs (02:00, 14:00 UTC). All trend and anomaly evaluation +is based on a rolling window of data samples, covering last 7 +days. -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. +Legend to table: -Proposed solution is to eliminate human factor and fully automate performance -trending, regression and progression detection, as well as bisecting. + - **Test Case** : name of CSIT test case, naming convention in + `CSIT wiki `_. + - **Trend [Mpps]** : last value of trend. + - **Short-Term Change [%]** : Relative change of last trend value vs. last + week trend value. + - **Long-Term Change [%]** : Relative change of last trend value vs. maximum + of trend values over the last quarter except last week. + - **Regressions [#]** : Number of regressions detected. + - **Progressions [#]** : Number of progressions detected. + - **Outliers [#]** : Number of outliers detected. -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. +All trend and anomaly calculations are defined in :ref:`trending_methodology`. -Continuous Performance Trending and Analysis --------------------------------------------- +Tested VPP worker-thread-core combinations (1t1c, 2t2c, 4t4c) are listed +in separate tables in section 1.x. Followed by trending methodology in +section 2. and daily trending graphs in sections 3.x. Daily trending +data used is provided in sections 4.x. -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. +VPP worker on 1t1c +------------------ -Trend Analysis -`````````````` +.. include:: ../../../_build/_static/vpp/performance-trending-dashboard-1t1c.rst -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). +VPP worker on 2t2c +------------------ -Analysis Metrics -```````````````` +.. include:: ../../../_build/_static/vpp/performance-trending-dashboard-2t2c.rst -Following statistical metrics are proposed as performance trend indicators over -the rolling window of last sets of historical measurement data: +VPP worker on 4t4c +------------------ - #. 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 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 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 - 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 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 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 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. +.. include:: ../../../_build/_static/vpp/performance-trending-dashboard-4t4c.rst