+.. _trending_methodology:
+
Trending Methodology
====================
-Continuous Trending and Analysis
---------------------------------
+Overview
+--------
This document describes a high-level design of a system for continuous
-measuring, trending and performance change detection for FD.io VPP SW
+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
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 maximum forwarding rate under the
-line rate packet load over a set trial duration, regardless of packet
-loss.
+(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:
- - 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).
+- **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.
-Performance Trend Analysis
---------------------------
+ - 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.
-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).
+- **Trial duration**: 10sec.
+- **Execution frequency**: twice a day, every 12 hrs (02:00, 14:00 UTC).
-Analysis Metrics
-````````````````
+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).
-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
-`````````````````
+Trend Analysis
+--------------
-Outlier evaluation of test result of value <X> follows the definition
-from previous section:
+All measured performance trend data is treated as time-series data that
+can be modelled as concatenation of groups, each group modelled
+using normal distribution. While sometimes the samples within a group
+are far from being distributed normally, we do not have a better tractable model.
-::
+The group boundaries are selected based on `Minimum Description Length`_.
- Outlier Evaluation Formula Evaluation Result
- ====================================================
- X < (Q1 - 1.5 * IQR) Outlier
- X >= (Q1 - 1.5 * IQR) Valid (For Trending)
+Minimum Description Length
+--------------------------
+
+`Minimum Description Length`_ (MDL) is a particular formalization
+of `Occam's razor`_ principle.
+
+The general formulation mandates to evaluate a large set of models,
+but for anomaly detection purposes, it is usefuls to consider
+a smaller set of models, so that scoring and comparing them is easier.
+
+For each candidate model, the data should be compressed losslessly,
+which includes model definitions, encoded model parameters,
+and the raw data encoded based on probabilities computed by the model.
+The model resulting in shortest compressed message is the "the" correct model.
+
+For our model set (groups of normally distributed samples),
+we need to encode group length (which penalizes too many groups),
+group average (more on that later), group stdev and then all the samples.
+
+Luckily, the "all the samples" part turns out to be quite easy to compute.
+If sample values are considered as coordinates in (multi-dimensional)
+Euclidean space, fixing stdev means the point with allowed coordinates
+lays on a sphere. Fixing average intersects the sphere with a (hyper)-plane,
+and Gaussian probability density on the resulting sphere is constant.
+So the only contribution is the "area" of the sphere, which only depends
+on the number of samples and stdev.
+
+A somehow ambiguous part is in choosing which encoding
+is used for group size, average and stdev.
+Diferent encodings cause different biases to large or small values.
+In our implementation we have chosen probability density
+corresponding to uniform distribution (from zero to maximal sample value)
+for stdev and average of the first group,
+but for averages of subsequent groups we have chosen a distribution
+which disourages deliminating groups with averages close together.
+
+One part of our implementation which is not precise enough
+is handling of measurement precision.
+The minimal difference in MRR values is currently 0.1 pps
+(the difference of one packet over 10 second trial),
+but the code assumes the precision is 1.0.
+Also, all the calculations assume 1.0 is totally negligible,
+compared to stdev value.
+
+The group selection algorithm currently has no parameters,
+all the aforementioned encodings and handling of precision is hardcoded.
+In principle, every group selection is examined, and the one encodable
+with least amount of bits is selected.
+As the bit amount for a selection is just sum of bits for every group,
+finding the best selection takes number of comparisons
+quadratically increasing with the size of data,
+the overall time complexity being probably cubic.
+
+The resulting group distribution looks good
+if samples are distributed normally enough within a group.
+But for obviously different distributions (for example `bimodal distribution`_)
+the groups tend to focus on less relevant factors (such as "outlier" density).
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.
+Once the trend data is divided into groups, each group has its population average.
+The start of the following group is marked as a regression (or progression)
+if the new group's average is lower (higher) then the previous group's.
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)]),
+quarterly), comparing the last group average AVG[last], to the one from week
+ago, AVG[last - 1week] and to the maximum of trend values over last
+quarter except last week, max(AVG[last - 3mths]..ANV[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 | Trend Change Formula | Value | Reference |
++=========================+=================================+===========+===========================================+
+| Short-Term Change | (Value - Reference) / Reference | AVG[last] | AVG[last - 1week] |
++-------------------------+---------------------------------+-----------+-------------------------------------------+
+| Long-Term Change | (Value - Reference) / Reference | AVG[last] | max(AVG[last - 3mths]..AVG[last - 1week]) |
++-------------------------+---------------------------------+-----------+-------------------------------------------+
Trend Presentation
------------------
-Trend Dashboard
-```````````````
+Performance Dashboard
+`````````````````````
Dashboard tables list a summary of per test-case VPP MRR performance
trend and trend compliance metrics and detected number of anomalies.
combinations (1t1c, 2t2c, 4t4c). Test case names are linked to
respective trending graphs for ease of navigation thru the test data.
-Trend Graphs
-``````````````
+Trendline Graphs
+````````````````
-Trends graphs show per test case measured MRR throughput values with
-associated trendlines. The graphs are constructed as follows:
+Trendline graphs show per test case measured MRR throughput values with
+associated gruop averages. 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:
+- 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.
+ - Regression - red circle.
+ - Progression - green circle.
+
+- The line shows average of each group.
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
-------------------------
+Jenkins Jobs
+------------
Performance Trending (PT)
`````````````````````````
-CSIT PT runs regular performance test jobs finding MRR per test case. PT
-is designed as follows:
+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 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)
`````````````````````````
-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:
+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:
- #. PA job triggers:
+ a) By PT job at its completion.
+ b) On-demand gerrit triggered.
- #. By PT job at its completion.
- #. On-demand gerrit triggered.
+2. Download and parse archived historical data and the new data:
- #. 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).
- #. 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).
+3. Re-calculate new groups and their averages.
- #. Calculate trend metrics for the rolling window of <N> sets of
- historical data:
+4. Evaluate new test 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) If the existing group is prolonged => Result = Pass,
+ Reason = Normal. (to be updated base on the final Jenkins code).
+ b) If a new group is detected with lower average => Result = Fail, Reason = Regression.
+ c) If a new group is detected with higher average => Result = Pass, Reason = Progression.
- #. Evaluate new test data against trend metrics:
+5. Generate and publish results
- #. 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.
+ 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/.
+
+Testbed HW configuration
+------------------------
- #. Generate and publish results
+The testbed HW configuration is described on
+`this FD.IO wiki page <https://wiki.fd.io/view/CSIT/CSIT_LF_testbed#FD.IO_CSIT_testbed_-_Server_HW_Configuration>`_.
- #. 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/.
+.. _Minimum Description Length: https://en.wikipedia.org/wiki/Minimum_description_length
+.. _Occam's razor: https://en.wikipedia.org/wiki/Occam%27s_razor
+.. _bimodal distribution: https://en.wikipedia.org/wiki/Bimodal_distribution
Code Review / csit.git / blobdiff