4 Continuous Trending and Analysis
5 --------------------------------
7 This document describes a high-level design of a system for continuous
8 measuring, trending and performance change detection for FD.io VPP SW
9 data plane. It builds upon the existing FD.io CSIT framework with
10 extensions to its throughput testing methodology, CSIT data analytics
11 engine (PAL – Presentation-and-Analytics-Layer) and associated Jenkins
14 Proposed design replaces existing CSIT performance trending jobs and
15 tests with new Performance Trending (PT) CSIT module and separate
16 Performance Analysis (PA) module ingesting results from PT and
17 analysing, detecting and reporting any performance anomalies using
18 historical trending data and statistical metrics. PA does also produce
19 trending dashboard and graphs with summary and drill-down views across
20 all specified tests that can be reviewed and inspected regularly by
21 FD.io developers and users community.
23 Performance Trending Tests
24 --------------------------
26 Performance trending is currently relying on the Maximum Receive Rate
27 (MRR) tests. MRR tests measure the maximum forwarding rate under the
28 line rate packet load over a set trial duration, regardless of packet
31 Current parameters for performance trending MRR tests:
33 - packet sizes: 64B (78B for IPv6 tests) for all tests, IMIX for
34 selected tests (vhost, memif).
35 - trial duration: 10sec.
36 - execution frequency: twice a day, every 12 hrs (02:00, 14:00 UTC).
38 Performance Trend Analysis
39 --------------------------
41 All measured performance trend data is treated as time-series data that
42 can be modelled using normal distribution. After trimming the outliers,
43 the median and deviations from median are used for detecting performance
44 change anomalies following the three-sigma rule of thumb (a.k.a.
50 Following statistical metrics are proposed as performance trend
51 indicators over the rolling window of last <N> sets of historical
54 - Q1, Q2, Q3 : Quartiles, three points dividing a ranked data set
55 into four equal parts, Q2 is the median of the data.
56 - IQR = Q3 - Q1 : Inter Quartile Range, measure of variability, used
57 here to calculate and eliminate outliers.
58 - Outliers : extreme values that are at least (1.5 * IQR) below Q1.
60 - Note: extreme values that are at least (1.5 * IQR) above Q3 are not
61 considered outliers, and are likely to be classified as
64 - TMA: Trimmed Moving Average, average across the data set of the
65 rolling window of <N> values without the outliers. Used here to
67 - TMSD: Trimmed Moving Standard Deviation, standard deviation over the
68 data set of the rolling window of <N> values without the outliers,
69 requires calculating TMA. Used for anomaly detection.
70 - TMM: Trimmed Moving Median, median across the data set of the rolling
71 window of <N> values with all data points, excluding the outliers.
72 Used as a trending value and as a reference for anomaly detection.
77 Outlier evaluation of test result of value <X> follows the definition
78 from previous section:
82 Outlier Evaluation Formula Evaluation Result
83 ====================================================
84 X < (Q1 - 1.5 * IQR) Outlier
85 X >= (Q1 - 1.5 * IQR) Valid (For Trending)
90 To verify compliance of test result of value <X> against defined trend
91 metrics and detect anomalies, three simple evaluation formulas are
96 Anomaly Compliance Evaluation
97 Evaluation Formula Confidence Level Result
98 =============================================================================
99 (TMM - 3 * TMSD) <= X <= (TMM + 3 * TMSD) 99.73% Normal
100 X < (TMM - 3 * TMSD) Anomaly Regression
101 X > (TMM + 3 * TMSD) Anomaly Progression
103 TMM is used for the central trend reference point instead of TMA as it
104 is more robust to anomalies.
109 Trend compliance metrics are targeted to provide an indication of trend
110 changes over a short-term (i.e. weekly) and a long-term (i.e.
111 quarterly), comparing the last trend value, TMM[last], to one from week
112 ago, TMM[last - 1week] and to the maximum of trend values over last
113 quarter except last week, max(TMM[(last - 3mths)..(last - 1week)]),
114 respectively. This results in following trend compliance calculations:
119 Compliance Metric Change Formula V(alue) R(eference)
120 =============================================================================================
121 Short-Term Change ((V - R) / R) TMM[last] TMM[last - 1week]
122 Long-Term Change ((V - R) / R) TMM[last] max(TMM[(last - 3mths)..(last - 1week)])
130 Dashboard tables list a summary of per test-case VPP MRR performance
131 trend and trend compliance metrics and detected number of anomalies.
133 Separate tables are generated for tested VPP worker-thread-core
134 combinations (1t1c, 2t2c, 4t4c). Test case names are linked to
135 respective trending graphs for ease of navigation thru the test data.
140 Trends graphs show per test case measured MRR throughput values with
141 associated trendlines. The graphs are constructed as follows:
143 - X-axis represents performance trend job build Id (csit-vpp-perf-mrr-
145 - Y-axis represents MRR throughput in Mpps.
146 - Markers to indicate anomaly classification:
148 - Outlier - gray circle around MRR value point.
149 - Regression - red circle.
150 - Progression - green circle.
152 In addition the graphs show dynamic labels while hovering over graph
153 data points, representing (trend job build Id, MRR value) and the actual
154 vpp build number (b<XXX>) tested.
157 Jenkins Jobs Description
158 ------------------------
160 Performance Trending (PT)
161 `````````````````````````
163 CSIT PT runs regular performance test jobs finding MRR per test case. PT
164 is designed as follows:
168 #. Periodic e.g. daily.
169 #. On-demand gerrit triggered.
171 #. Measurements and calculations per test case:
173 #. MRR Max Received Rate
175 #. Measured: Unlimited tolerance of packet loss.
176 #. Send packets at link rate, count total received packets, divide
177 by test trial period.
179 #. Archive MRR per test case.
180 #. Archive all counters collected at MRR.
182 Performance Analysis (PA)
183 `````````````````````````
185 CSIT PA runs performance analysis including trending and anomaly
186 detection using specified trend analysis metrics over the rolling window
187 of last <N> sets of historical measurement data. PA is defined as
192 #. By PT job at its completion.
193 #. On-demand gerrit triggered.
195 #. Download and parse archived historical data and the new data:
197 #. Evalute new data from latest PT job against the rolling window of
198 <N> sets of historical data.
199 #. Download RF output.xml files and compressed archived data.
200 #. Parse out the data filtering test cases listed in PA specification
201 (part of CSIT PAL specification file).
203 #. Calculate trend metrics for the rolling window of <N> sets of
206 #. Calculate quartiles Q1, Q2, Q3.
207 #. Trim outliers using IQR.
208 #. Calculate TMA and TMSD.
209 #. Calculate normal trending range per test case based on TMM and TMSD.
211 #. Evaluate new test data against trend metrics:
213 #. If within the range of (TMA +/- 3*TMSD) => Result = Pass,
215 #. If below the range => Result = Fail, Reason = Regression.
216 #. If above the range => Result = Pass, Reason = Progression.
218 #. Generate and publish results
220 #. Relay evaluation result to job result.
221 #. Generate a new set of trend summary dashboard and graphs.
222 #. Publish trend dashboard and graphs in html format on https://docs.fd.io/.