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Diffstat (limited to 'docs/cpta')
| -rw-r--r-- | docs/cpta/methodology/index.rst | 161 |
1 files changed, 80 insertions, 81 deletions
diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst index 227dfbcb02..8bff734e4a 100644 --- a/docs/cpta/methodology/index.rst +++ b/docs/cpta/methodology/index.rst @@ -33,12 +33,11 @@ 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 +- **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 +- **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. @@ -48,8 +47,8 @@ Current parameters for performance trending MRR tests: 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). +- **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 @@ -72,24 +71,25 @@ 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. +- **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. +- **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 ````````````````` @@ -97,12 +97,13 @@ 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) ++----------------------------+----------------------+ +| Outlier Evaluation Formula | Evaluation Result | ++============================+======================+ +| X < (Q1 - 1.5 * IQR) | Outlier | ++----------------------------+----------------------+ +| X >= (Q1 - 1.5 * IQR) | Valid (For Trending) | ++----------------------------+----------------------+ Anomaly Detection ````````````````` @@ -111,14 +112,16 @@ To verify compliance of test result of valid 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 ++-------------------------------------------+------------------+-------------+ +| 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. @@ -133,13 +136,14 @@ 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 | | | | +| 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 ------------------ @@ -183,21 +187,18 @@ 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: +1. PT job triggers: - - Periodic e.g. daily. - - On-demand gerrit triggered. + a) Periodic e.g. daily. + b) On-demand gerrit triggered. -#. Measurements and data calculations per test case: +2. Measurements and data calculations per test case: - - MRR Max Received Rate + a) Max Received Rate (MRR) - send packets at link rate over a trial + period, count total received packets, divide by trial period. - - 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. +3. Archive MRR per test case. +4. Archive all counters collected at MRR. Performance Analysis (PA) ````````````````````````` @@ -207,44 +208,42 @@ 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: -#. PA job triggers: - - - By PT job at its completion. - - On-demand gerrit triggered. - -#. Download and parse archived historical data and the new data: +1. PA job triggers: - - Download RF output.xml files from latest PT job and compressed - archived data. + a) By PT job at its completion. + b) On-demand gerrit triggered. - - Parse out the data filtering test cases listed in PA specification - (part of CSIT PAL specification file). +2. 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 for trendline calculation, anomaly - detection and short-term trend compliance. And against long-term - trendline metrics for long-term trend compliance. + 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. -#. Calculate trend metrics for the rolling window of <N> sets of +3. 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. + 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. -#. Evaluate new test data against trend metrics: +4. 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. + 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. -#. Generate and publish results +5. Generate and publish results - - 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/. + 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/. |