7.7 KiB

Raw Blame History

Build a new optimization strategy

Watcher Decision Engine has an external strategy <strategy_definition> plugin interface which gives anyone the ability to integrate an external strategy in order to make use of placement algorithms.

This section gives some guidelines on how to implement and integrate custom strategies with Watcher.

Pre-requisites

Before using any strategy, you should make sure you have your Telemetry service configured so that it would provide you all the metrics you need to be able to use your strategy.

Creating a new plugin

First of all you have to:

Extend :py~.BaseStrategy
Implement its :py~.BaseStrategy.execute method

Here is an example showing how you can write a plugin called DummyStrategy:

# Filepath = third-party/thirdparty/dummy.py
# Import path = thirdparty.dummy
import uuid

class DummyStrategy(BaseStrategy):

    DEFAULT_NAME = "dummy"
    DEFAULT_DESCRIPTION = "Dummy Strategy"

    def __init__(self, name=DEFAULT_NAME, description=DEFAULT_DESCRIPTION):
        super(DummyStrategy, self).__init__(name, description)

    def execute(self, model):
        migration_type = 'live'
        src_hypervisor = 'compute-host-1'
        dst_hypervisor = 'compute-host-2'
        instance_id = uuid.uuid4()
        parameters = {'migration_type': migration_type,
                      'src_hypervisor': src_hypervisor,
                      'dst_hypervisor': dst_hypervisor}
        self.solution.add_action(action_type="migration",
                                 resource_id=instance_id,
                                 input_parameters=parameters)
        # Do some more stuff here ...
        return self.solution

As you can see in the above example, the :py~.BaseStrategy.execute method returns a :py~.BaseSolution instance as required. This solution is what wraps the abstract set of actions the strategy recommends to you. This solution is then processed by a planner <planner_definition> to produce an action plan which shall contain the sequenced flow of actions to be executed by the Watcher Applier <watcher_applier_definition>.

Please note that your strategy class will be instantiated without any parameter. Therefore, you should make sure not to make any of them required in your __init__ method.

Abstract Plugin Class

Here below is the abstract :py~.BaseStrategy class that every single strategy should implement:

watcher.decision_engine.strategy.strategies.base.BaseStrategy

Add a new entry point

In order for the Watcher Decision Engine to load your new strategy, the strategy must be registered as a named entry point under the watcher_strategies entry point of your setup.py file. If you are using pbr, this entry point should be placed in your setup.cfg file.

The name you give to your entry point has to be unique.

Here below is how you would proceed to register DummyStrategy using pbr:

[entry_points]
watcher_strategies =
    dummy = thirdparty.dummy:DummyStrategy

To get a better understanding on how to implement a more advanced strategy, have a look at the :py~.BasicConsolidation class.

Using strategy plugins

The Watcher Decision Engine service will automatically discover any installed plugins when it is restarted. If a Python package containing a custom plugin is installed within the same environment as Watcher, Watcher will automatically make that plugin available for use.

At this point, Watcher will use your new strategy if you reference it in the goals under the [watcher_goals] section of your watcher.conf configuration file. For example, if you want to use a dummy strategy you just installed, you would have to associate it to a goal like this:

[watcher_goals]
goals = BALANCE_LOAD:basic,MINIMIZE_ENERGY_CONSUMPTION:dummy

You should take care when installing strategy plugins. By their very nature, there are no guarantees that utilizing them as is will be supported, as they may require a set of metrics which is not yet available within the Telemetry service. In such a case, please do make sure that you first check/configure the latter so your new strategy can be fully functional.

Querying metrics

A large set of metrics, generated by OpenStack modules, can be used in your strategy implementation. To collect these metrics, Watcher provides a Helper to the Ceilometer API, which makes this API reusable and easier to used.

If you want to use your own metrics database backend, please refer to the Ceilometer developer guide. Indeed, Ceilometer's pluggable model allows for various types of backends. A list of the available backends is located here. The Ceilosca project is a good example of how to create your own pluggable backend.

Finally, if your strategy requires new metrics not covered by Ceilometer, you can add them through a Ceilometer plugin.

Read usage metrics using the Python binding

You can find the information about the Ceilometer Python binding on the OpenStack ceilometer client python API documentation

To facilitate the process, Watcher provides the osc attribute to every strategy which includes clients to major OpenStack services, including Ceilometer. So to access it within your strategy, you can do the following:

# Within your strategy "execute()"
cclient = self.osc.ceilometer
# TODO: Do something here

Using that you can now query the values for that specific metric:

query = None  # e.g. [{'field': 'foo', 'op': 'le', 'value': 34},]
value_cpu = cclient.samples.list(
    meter_name='cpu_util',
    limit=10, q=query)

Read usage metrics using the Watcher Cluster History Helper

Here below is the abstract BaseClusterHistory class of the Helper.

watcher.metrics_engine.cluster_history.api.BaseClusterHistory

The following code snippet shows how to create a Cluster History class:

from watcher.metrics_engine.cluster_history import ceilometer as ceil

query_history  = ceil.CeilometerClusterHistory()