diff options
Diffstat (limited to 'docs/cpta')
| -rw-r--r-- | docs/cpta/methodology/index.rst | 222 |
1 files changed, 220 insertions, 2 deletions
diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst index a218f73d60..8354943186 100644 --- a/docs/cpta/methodology/index.rst +++ b/docs/cpta/methodology/index.rst @@ -1,3 +1,221 @@ -Methodology -=========== +Trending Methodology +==================== +Continuous Trending and Analysis +-------------------------------- + +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 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 Trending Tests +-------------------------- + +Performance trending is currently relying on the Maximum Receive Rate +(MRR) tests. MRR tests measure the maximum forwarding rate under the +line rate packet load over a set trial duration, regardless of packet +loss. + +Current parameters for performance trending MRR tests: + + - packet sizes: 64B (78B for IPv6 tests) for all tests, IMIX for + selected tests (vhost, memif). + - trial duration: 10sec. + - execution frequency: twice a day, every 12 hrs (02:00, 14:00 UTC). + +Performance 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). + +Analysis Metrics +```````````````` + +Following statistical metrics are proposed 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 + 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 the + rolling window of <N> values without the outliers. Used here to + calculate TMSD. + - TMSD: Trimmed Moving Standard Deviation, standard deviation over the + data set of the rolling window of <N> values without the outliers, + requires calculating TMA. Used for anomaly detection. + - TMM: Trimmed Moving Median, median across the data set of the rolling + window of <N> values with all data points, 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 value <X> 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 +------------------ + +Trend 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. + +Trend Graphs +`````````````` + +Trends 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 Description +------------------------ + +Performance Trending (PT) +````````````````````````` + +CSIT PT runs regular performance test jobs finding MRR per test case. PT +is designed as follows: + + #. PT job triggers: + + #. Periodic e.g. daily. + #. On-demand gerrit triggered. + + #. 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. + + #. Archive MRR per test case. + #. Archive all counters collected at MRR. + +Performance Analysis (PA) +````````````````````````` + +CSIT PA runs performance analysis including trending 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: + + #. PA job triggers: + + #. By PT job at its completion. + #. On-demand gerrit triggered. + + #. Download and parse archived historical data and the new data: + + #. Evalute new data from latest PT job 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 TMM 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 summary dashboard and graphs. + #. Publish trend dashboard and graphs in html format on https://docs.fd.io/. |