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@@ -1,10 +1,10 @@
-Performance Trending Methodology
-================================
-
.. _trending_methodology:
-Continuous Trending and Analysis
---------------------------------
+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
@@ -22,8 +22,8 @@ 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 Tests
+-----------------
Performance trending is currently relying on the Maximum Receive Rate
(MRR) tests. MRR tests measure the packet forwarding rate under the
@@ -51,13 +51,14 @@ Current parameters for performance trending MRR tests:
- Trial duration: 10sec.
- Execution frequency: twice a day, every 12 hrs (02:00, 14:00 UTC).
-In the future if tested VPP configuration can handle the packet rate
-higher than bi-directional 10GE link rate, e.g. all IMIX tests and
-64B/78B multi-core tests, a higher maximum load will be offered
-(25GE|40GE|100GE).
+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).
-Performance Trend Analysis
---------------------------
+Trend Analysis
+--------------
All measured performance trend data is treated as time-series data that
can be modelled using normal distribution. After trimming the outliers,
@@ -65,12 +66,11 @@ 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
+Metrics
````````````````
-Following statistical metrics are proposed as performance trend
-indicators over the rolling window of last <N> sets of historical
-measurement data:
+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.
@@ -135,8 +135,8 @@ respectively. This results in following trend compliance calculations:
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)])
-Performance Trend Presentation
-------------------------------
+Trend Presentation
+------------------
Performance Dashboard
`````````````````````
@@ -168,8 +168,8 @@ data points, representing (trend job build Id, MRR value) and the actual
vpp build number (b<XXX>) tested.
-Jenkins Jobs Description
-------------------------
+Jenkins Jobs
+------------
Performance Trending (PT)
`````````````````````````
@@ -231,13 +231,14 @@ PA is defined as follows:
#. Evaluate new test data against trend metrics:
#. If within the range of (TMA +/- 3*TMSD) => Result = Pass,
- Reason = Normal. (to be updated base on final Jenkins code)
+ 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
- #. Relay evaluation result to job result. (to be updated base on final
- Jenkins code)
+ #. 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/.
+ #. Publish trend dashboard and graphs in html format on
+ https://docs.fd.io/.