Merge "Define performance test suite framework"
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This work is licensed under a Creative Commons Attribution 3.0 Unported
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License. http://creativecommons.org/licenses/by/3.0/legalcode
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..
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============================================
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StarlingX: Performance measurement framework
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============================================
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Storyboard: https://storyboard.openstack.org/#!/story/2006406
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The measurement of performance metrics in edge cloud systems is a key factor in
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quality assurance. Having a common understanding and a practical framework to
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run the performance tests are important and necessary for users and developers
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in the community. This specification will describe the scope of the performance
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framework, use cases and how it can scale by new tests developed by the
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community or imported from existing test performance frameworks.
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Problem description
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===================
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The deployment of StarlingX could happen in a wide variability of hardware and
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network environments, these differences could result in a difference in
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performance metrics. As of today, the community does not have a consolidated
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performance framework to measure the performance metrics with specific and
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well-defined test cases.
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Use Cases
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---------
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Developers who made some fairly significant changes in codebase want to ensure
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no performance regression brought by the changes. Developers who did some
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performance improvements need to measure the performance result with a
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consolidating framework valid across the community. Some of the community
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members want to measure out the performance items on StarlingX and promote
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StarlingX advantages in terms of performance which are relevant to an edge
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solution. Potential users want to evaluate StarlingX as one of their Edge
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solution candidates, so offering such a performance framework will be a
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positive factor of contributing to the evaluation process.
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Proposed change
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===============
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The proposed change is to create a set of scripts and documentation under the
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starlingx testing repository (https://opendev.org/starlingx/test) that anyone
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can use to measure the metric they need under their hardware and software
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environment.
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Alternatives
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------------
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The community can use existing performance test frameworks, Some of those are:
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* Rally:
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OpenStack project, dedicated to the performance analysis and benchmarking
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system of individual OpenStack components
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* Kubernetes perf-tests:
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An open source project dedicated to Kubernetes-related performance test tools
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* Yardstick by OPNFV:
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The Yardstick concept decomposes typical virtual network function work-load
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performance metrics into several characteristics/performance vectors, which
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each of them can be represented by distinct test-cases.
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The disadvantage of all these frameworks is that they coexist in separate
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projects. Users need to go and find the test cases anywhere on the internet, is
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not a centralized and scalable solution. The StarlingX Performance measurement
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framework proposed will allow the importing of existing test cases to be
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re-used in a centralized system easy to use for the community.
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Data model impact
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-----------------
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None
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REST API impact
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---------------
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None
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Security impact
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---------------
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None
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Other end-user impact
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---------------------
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End users will interact with this framework by command line on the controller
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system by the performance-test-runner.py script, which is the entry point to
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select and configure the test case. An example of to launch a test case might
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be:
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::
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./performance-test-runner.py --test failed_vm_detection --compute=0
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This will generate results of multiple runs in CSV format easy to post-process.
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Performance Impact
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------------------
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Running this performance framework or test cases, there shouldn’t be a
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performance impact or obvious overhead on the system.
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Other deployer impact
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---------------------
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None, there will be no impact on how to deploy and configure StarlingX
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Developer impact
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----------------
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There will be a good impact on the developers. Since they will be able to have
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a consolidated and well aligned framework to measure the performance impact of
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their code/configuration changes.
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Developers can't improve something if they don’t know it needs improvement.
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That’s where this performance testing framework comes in. It gives the
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developers tools to detect how fast, stable, and scalable their StarlingX, is
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so they can decide if it needs to improve or not.
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Upgrade impact
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--------------
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None
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Implementation
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==============
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The test framework will have the next components on the implementation
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* Common scripts:
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This directory will contain scripts to capture key timestamps or collect other
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key data along with some use cases so that developers can measure out the time
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(latency) or other performance data (such as throughput, system CPU utilization
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and so on). Any common script that any other test case could use should be in
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this directory.
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Despite the existence of a directory for common scripts, each one of the test
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cases is responsible for its own metrics. One example is the time measurement
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of test cases that run in more than one node. For these kinds of test cases
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will be necessary to use synchronization protocols like the Network Time
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Protocol (NTP). The Network Time Protocol (NTP) is a networking protocol for
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clock synchronization between computer systems. NTP is intended to synchronize
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all participating computers to within a few milliseconds of Coordinated
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Universal Time (UTC).In some other cases, it might not be necessary to have a
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protocol for clock synchronization.
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Most of the details for wether to re-use a common script form the common
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directory or handle the measurement by the test itself will be left to the test
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implementation
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* Performance-test-runner.py:
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The main command-line interface to execute the test cases on the system under
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test
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* Test cases directory:
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This directory can contain wrapper scripts to existing upstream performance
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test cases as well as new test cases specific for Starlting X.
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A view of the directory layout will look like:
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::
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performance/
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├── common
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│ ├── latency_metrics.py
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│ ├── network_generator.py
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│ ├── ntp_metrics.py
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│ └── statistics.py
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└── tests_cases
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│ ├── failed_compute_detection.py
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│ ├── failed_control_detection.py
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│ ├── failed_network_detection.py
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│ ├── failed_vm_detection.py
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│ ├── neutron_test_case_1.py
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│ ├── opnfv_test_case_1.py
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│ ├── opnfv_test_case_2.py
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│ ├── rally_test_case_1.py
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│ ├── rally_test_case_2.py
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│
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└── performance-test-runner.py
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The goal is that anyone on the StarlingX community can either define
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performance tests cases or re use existing on other projects and create
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scripts to measure in a scalable and repeatable framework.
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In the future, it might be possible to evaluate how to connect some basic
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performance tests cases to zuul to detect regressions on commits to be merged.
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This will require to set up a robust infrastructure to run the sanity
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performance test cases for each commit to be merged.
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Assignee(s)
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-----------
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Wang Hai Tao
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Elio Martinez
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Juan Carlos Alonzo
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Victor Rodriguez
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Repos Impacted
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--------------
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https://opendev.org/starlingx/test
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Work Items
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----------
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* Develop automated scripts for core test cases, such as:
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* Detection of failed VM
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* Detection failed compute node
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* Controller/Node fail detect/recovery
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* Detection of network link fail
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* DPDK Live Migrate Latency
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* Avg/Max host/guest Latency (Cyclictest)
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* Swact Time
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* Develop performance-test-runner.py that call each test case script
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* Integrate performance-test-runner.py with pytest framework
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* Implement call clone of opnfv test cases
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* Implement execution of opnfv test case
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* Implement monitoring pipeline to automatically detect changes in upstream
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* Automate weekly mail with list of upstream performance test cases that change
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* Implement automatic test cases documentation based on pydoc
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* Document framework on StarlingX wiki
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* Send regular status to ML and demos on community call for feedback
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Dependencies
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============
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None
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Testing
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=======
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None
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Documentation Impact
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====================
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The performance test cases will be documented with pydoc
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(https://docs.python.org/3.0/library/pydoc.html). The pydoc module
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automatically generates documentation from Python modules. The documentation
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can be presented as pages of text on the console, served to a Web browser, or
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saved to HTML files.
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The goal is to generate automatically the documentation for the end-user
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testing guide. This methodology will catch when the community adds or modify a
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new performance test case. At the same time if a standard config option changes
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or is deprecated the test framework documentation will be updated to reflect
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the change.
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References
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==========
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History
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=======
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.. list-table:: Revisions
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:header-rows: 1
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* - Release Name
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- Description
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* - Stein
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- Introduced
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