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.. _trending_methodology:
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
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 Tests
-----------------
Performance trending is currently relying on the Maximum Receive Rate
(MRR) tests. MRR tests measure the packet forwarding rate under the
maximum load offered by traffic generator over a set trial duration,
regardless of packet loss. Maximum load for specified Ethernet frame
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
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
tested, with the actual packet rate depending on frame size,
transmission overhead and traffic generator NIC forwarding capacity.
- For 10GE NICs the maximum packet rate load is 2* 14.88 Mpps for 64B,
a 10GE bi-directional link rate.
- For 40GE NICs the maximum packet rate load is 2* 18.75 Mpps for 64B,
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).
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).
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).
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.
- 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.
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 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 |
+-------------------------------------------+------------------+-------------+
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
------------------
Performance 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.
Trendline Graphs
````````````````
Trendline 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
------------
Performance Trending (PT)
`````````````````````````
CSIT PT runs regular performance test jobs measuring and collecting MRR
data per test case. PT is designed as follows:
1. PT job triggers:
a) Periodic e.g. daily.
b) On-demand gerrit triggered.
2. Measurements and data calculations per test case:
a) Max Received Rate (MRR) - send packets at link rate over a trial
period, count total received packets, divide by trial period.
3. Archive MRR per test case.
4. Archive all counters collected at MRR.
Performance Analysis (PA)
`````````````````````````
CSIT PA runs performance analysis including trendline calculation, trend
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:
1. PA job triggers:
a) By PT job at its completion.
b) On-demand gerrit triggered.
2. Download and parse archived historical data and the new data:
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.
3. Calculate trend metrics for the rolling window of <N> sets of
historical data:
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.
4. Evaluate new test data against trend metrics:
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.
5. Generate and publish results
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/.
|
