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-rw-r--r--docs/cpta/methodology/index.rst161
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/.