New section: trending analysis methodology

Change-Id: I09ec04759a5f1f01c3835349c3ae094ae459226e
Signed-off-by: Maciek Konstantynowicz <mkonstan@cisco.com>
(cherry picked from commit 2cc56891f5e5b399bd5fbe761c64fcd937e46750)

1 files changed, 220 insertions, 2 deletions

diff --git a/docs/cpta/methodology/index.rst b/docs/cpta/methodology/index.rst
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-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/.