watcher/doc/source/dev/plugins.rst

Watcher plugins

Writing a Watcher Decision Engine plugin

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

This section gives some guidelines on how to implement and integrate custom Stategies 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 the base BaseStrategy class
• Implement its execute method

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

# Filepath = third-party/third_party/dummy.py
# Import path = third_party.dummy

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):
        self.solution.add_change_request(
            Migrate(vm=my_vm, src_hypervisor=src, dest_hypervisor=dest)
        )
        # Do some more stuff here ...
        return self.solution

As you can see in the above example, the execute() method returns a solution as required.

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 BaseStrategy class that every single strategy should implement:

watcher.decision_engine.strategy.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 = third_party.dummy:DummyStrategy

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

Using strategy plugins

The Watcher Decision Engine service will automatically discover any installed plugins when it is run. 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, the way 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

The metrics available depend on the hypervisors that OpenStack manages on the specific implementation. You can find the metrics available per hypervisor and OpenStack release on the OpenStack site.

There are different possible ways to obtain usage metrics in Watcher, you can use the default Ceilometer API or our Helper. The Helper attempted to make the Ceilometer API more reusable and easy to use.

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

The first step is to authenticate against the Ceilometer service (assuming that you already imported the Ceilometer client for Python) with this call:

cclient = ceilometerclient.client.get_client(VERSION, os_username=USERNAME,
 os_password=PASSWORD, os_tenant_name=PROJECT_NAME, os_auth_url=AUTH_URL)

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

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 snippet code shows how to create a Cluster History class:

query_history  = CeilometerClusterHistory()

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

query_history.statistic_aggregation(resource_id=hypervisor.uuid,
                              meter_name='compute.node.cpu.percent',
                              period="7200",
                              aggregate='avg'
                              )