X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=docs%2Fcpta%2Fmethodology%2Findex.rst;h=9105ec46b4c7c20fe6ee48d22d05b018dbd259fc;hp=5efdfaae32ee7332af562a5303694a642205d7c6;hb=441d07d2b1be0bf7b9f6fbd917fdd89aeb4fb253;hpb=1a72adeb35bfd540f882a107ed1007e4a8545dec diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst index 5efdfaae32..9105ec46b4 100644 --- a/docs/cpta/methodology/index.rst +++ b/docs/cpta/methodology/index.rst @@ -1,244 +1,8 @@ -.. _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 sets of historical measurement data: - -- Q1, Q2, Q3 : Quartiles, three points dividing a ranked data set - of 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 - values without the outliers. Used here to calculate TMSD. -- TMSD : Trimmed Moving Standard Deviation, standard deviation over the - data set of values without the outliers, - requires calculating TMA. Used for anomaly detection. -- TMM : Trimmed Moving Median, median across the data set of 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 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 against defined -trend metrics and detect anomalies, three simple evaluation formulas are -used: - - Anomaly Compliance Evaluation - Evaluation Formula Confidence Level 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 Change Formula V(alue) R(eference) - ============================================================================================= - Short-Term Change ((V - R) / R) TMM[last] TMM[last - 1week] - Long-Term Change ((V - R) / R) 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) 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: - -#. PT job triggers: - - #. Periodic e.g. daily. - #. On-demand gerrit triggered. - -#. Measurements and data 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. - -#. Archive MRR per test case. -#. 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 sets of historical measurement data. -PA is defined as follows: - -#. PA job triggers: - - #. By PT job at its completion. - #. On-demand gerrit triggered. - -#. Download and parse archived historical data and the new data: - - #. Download RF output.xml files from latest PT job and compressed - archived data. - - #. Parse out the data filtering test cases listed in PA specification - (part of CSIT PAL specification file). - - #. Evalute new data from latest PT job against the rolling window of - 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. - -#. 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 TMM and - TMSD. - -#. Evaluate new test data against trend metrics: - - #. If within the range of (TMA +/- 3*TMSD) => Result = Pass, - Reason = Normal. (to be updated base on the final Jenkins code). - #. If below the range => Result = Fail, Reason = Regression. - #. If above the range => Result = Pass, Reason = Progression. - -#. Generate and publish results +.. toctree:: - #. Relay evaluation result to job result. (to be updated base on the - final Jenkins code). - #. Generate a new set of trend summary dashboard and graphs. - #. Publish trend dashboard and graphs in html format on - https://docs.fd.io/. + overview + trend_analysis + trend_presentation