Specificiation for time series framework and analysis
Introduces a specification which would allow for more intricate analysis by strategies using simple time series analysis. Change-Id: Icac8fd3e5736a374cde026a42f0fe9195e30fdac
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This work is licensed under a Creative Commons Attribution 3.0 Unported
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License.
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http://creativecommons.org/licenses/by/3.0/legalcode
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=====================
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Time Series Framework
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=====================
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https://blueprints.launchpad.net/watcher/+spec/time-series-framework
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Strategies are currently limited to obtain information about metrics in
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an aggregated form for the most recent measurements. This limits what
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strategies can achieve as they are unable to retrieve information about past
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occurrences or information about periodic patterns. Currently, an audit will
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have to be launched at the same time a specific situation is occurring in order
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for the strategy to be able to identify this. A time series framework will
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allow datasources to provide metrics over specific periods without aggregation,
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allowing strategies to detect periodic patterns such as a weekly contention and
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resolve these accordingly.
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Problem description
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===================
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Strategies can only obtain metrics about the current state and not retrieve any
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metrics over specific periods, in addition, strategies can only obtain an
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aggregated value over the entire period instead of obtaining a time series.
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Effectively, this limits the usability of strategies as they are unable to
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detect any periodic patterns or resolves issues which occur sporadically.
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Combined with that it generally takes a long time to run an audit this
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complicates detecting problems.
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Use Cases
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----------
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- As a user I want strategies to detect current and past problems and optimize
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the infrastructure accordingly.
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- As a developer I want to make more effective strategies that can detect
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periodic patterns and make more informed decisions.
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- As a developer I want to develop an external machine learning component to
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integrate with Watcher but still use already existing components of Watcher
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effectively.
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Proposed change
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===============
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To enable time series the datasource base class will need a new method. This
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method will allow metrics to be retrieved over a specific period with a
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specific granularity. This method will return a list of values according to the
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supplied parameters and will subsequently be implemented by all current
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datasources. An example of how the interface of such a method could look is
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shown below.
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.. code-block:: python
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@abc.abstractmethod
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def statistic_series(self, resource, resource_type, meter_name, start_time,
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end_time, granularity):
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"""Retrieve metrics based on the specified parameters of a period of time
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:param resource: The object returned by clients such as Server or
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Hypersivor when calling nova.servers.get or nova.hypervisors.get
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:param resource_type: The Type of the resource object selected from
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RESOURCE_TYPES.
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:param start_time: The datetime to start retrieving metrics for
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:type start_time: datetime.datetime
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:param end_time: The datetime to limit the retrieval of metrics to
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:type end_time: datetime.datetime
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:param granularity: Interval between collected data in seconds.
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:return: Dictionary of key value pairs with timestamps and metric values
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"""
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In addition, a new class will be added that can utilize a datasource to perform
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time series analysis. Features will include, decomposing trends, periodic
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variations (daily, weekly, seasonal) and irregular variations (residuals). As
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well as determining the stationarity. Any machine learning aspects such as
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predicting future values through models like ARIMA will **not** be part of this
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time series class.
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A best effort should be made to find a lightweight library that implements the
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desired time series functionality as libraries such as `numpy`_ or
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`scikit-learn`_ are particularly large and offer substantially more
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functionality than is required. If no suitable library can be found the
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functionality will be implemented without the use of a third-party library.
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.. _numpy: https://numpy.org/
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.. _scikit-learn: https://scikit-learn.org/stable/index.html
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Alternatives
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------------
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- The time series analysis along with any potential machine learning features
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could in its entirety be developed as an external service that integrates
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with Watcher. This would allow the use of libraries such as numpy and
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scikit-learn as they are very likely desired dependencies in a machine
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learning service. The time series method for datasources will still have to
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be implemented but the class for time series decomposition can be removed.
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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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Notifications 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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None
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Performance Impact
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------------------
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Collecting lists of values instead of single values per node and or instance
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can significantly increase the memory consumption of Watcher. Especially, when
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audits are run for large collections of nodes and instances. Additionally, the
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transmission of all this data could potentially be a bottleneck if executed
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sequentially. For this the time series class could utilize the `decision-engine
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threadpool`_ to execute multiple requests in parallel.
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.. _decision-engine threadpool: https://docs.openstack.org/watcher/latest/contributor/concurrency.html
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Other deployer impact
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---------------------
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None
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Developer impact
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----------------
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This change will expose a new method in datasources which could potentially be
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used to enable machine learning features in external projects that integrate
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with Watcher.
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Implementation
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==============
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Assignee(s)
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-----------
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Primary assignee:
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<dantalion>
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Work Items
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----------
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- Introduce new method to datasource baseclass
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- Implement new base method in all datasources
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- Evaluate available lightweight time series libraries
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- Implement simple time series class
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Dependencies
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============
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* Potentially a new lightweight time series library will be added as
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dependency.
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Testing
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=======
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- Unit tests for the new method such as validating the specified period
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- Unit tests to verify the time series decompositions using known dummy data
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- Unit tests to verify stationarity using known dummy data.
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- Possible integration tests for testing the retrieval of time series metrics
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from datasources.
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Documentation Impact
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====================
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No additional documentation is required apart from documenting newly
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introduced methods and classes.
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References
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==========
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- `decision-engine threadpool`_
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- `numpy`_
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- `scikit-learn`_
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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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* - Victoria
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- Introduced
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