Updated DE architecture doc + 'period' param
In this changeset, I updated the architecture documentation about the Decision Engine by adding a new sequence diagram outlining the CDM synchronization workflow. I also explained the default 'period' parameter used in the CDMC plugin. Change-Id: I09790281ba9117e302ab8e66a887667929c6c261 Partially-Implements: blueprint cluster-model-objects-wrapper
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@ -176,30 +176,42 @@ associated :ref:`Audit Template <audit_template_definition>` and knows the
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:ref:`Goal <goal_definition>` to achieve.
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It then selects the most appropriate :ref:`Strategy <strategy_definition>`
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depending on how Watcher was configured for this :ref:`Goal <goal_definition>`.
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from the list of available strategies achieving this goal.
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The :ref:`Strategy <strategy_definition>` is then dynamically loaded (via
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`stevedore <https://github.com/openstack/stevedore/>`_). The
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:ref:`Watcher Decision Engine <watcher_decision_engine_definition>` calls the
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**execute()** method of the :ref:`Strategy <strategy_definition>` class which
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generates a solution composed of a set of :ref:`Actions <action_definition>`.
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`stevedore <http://docs.openstack.org/developer/stevedore/>`_). The
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:ref:`Watcher Decision Engine <watcher_decision_engine_definition>` executes
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the strategy.
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In order to compute the potential :ref:`Solution <solution_definition>` for the
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Audit, the :ref:`Strategy <strategy_definition>` relies on different sets of
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data:
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- :ref:`Cluster data models <cluster_data_model_definition>` that are
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periodically synchronized through pluggable cluster data model collectors.
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These models contain the current state of various
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:ref:`Managed resources <managed_resource_definition>` (e.g., the data stored
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in the Nova database). These models gives a strategy the ability to reason on
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the current state of a given :ref:`cluster <cluster_definition>`.
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- The data stored in the :ref:`Cluster History Database
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<cluster_history_db_definition>` which provides information about the past of
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the :ref:`Cluster <cluster_definition>`.
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Here below is a sequence diagram showing how the Decision Engine builds and
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maintains the :ref:`cluster data models <cluster_data_model_definition>` that
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are used by the strategies.
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.. image:: ./images/sequence_architecture_cdmc_sync.png
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:width: 100%
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The execution of a strategy then yields a solution composed of a set of
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:ref:`Actions <action_definition>` as well as a set of :ref:`efficacy
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indicators <efficacy_indicator_definition>`.
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These :ref:`Actions <action_definition>` are scheduled in time by the
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:ref:`Watcher Planner <watcher_planner_definition>` (i.e., it generates an
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:ref:`Action Plan <action_plan_definition>`).
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In order to compute the potential :ref:`Solution <solution_definition>` for the
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Audit, the :ref:`Strategy <strategy_definition>` relies on two sets of data:
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- the current state of the
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:ref:`Managed resources <managed_resource_definition>`
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(e.g., the data stored in the Nova database)
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- the data stored in the
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:ref:`Cluster History Database <cluster_history_db_definition>`
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which provides information about the past of the
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:ref:`Cluster <cluster_definition>`
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.. _data_model:
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Data model
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@ -216,8 +228,6 @@ Here below is a diagram representing the main objects in Watcher from a
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database perspective:
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.. image:: ./images/watcher_db_schema_diagram.png
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:width: 100%
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.. _sequence_diagrams:
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@ -114,12 +114,15 @@ tune the action to its needs. To do so, you can implement the
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def execute(self):
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assert self.config.test_opt == 0
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def get_config_opts(self):
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return [
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@classmethod
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def get_config_opts(cls):
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return super(
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DummyAction, cls).get_config_opts() + [
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cfg.StrOpt('test_opt', help="Demo Option.", default=0),
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# Some more options ...
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]
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The configuration options defined within this class method will be included
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within the global ``watcher.conf`` configuration file under a section named by
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convention: ``{namespace}.{plugin_name}``. In our case, the ``watcher.conf``
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@ -54,6 +54,10 @@ Define configuration parameters
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===============================
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At this point, you have a fully functional cluster data model collector.
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By default, cluster data model collectors define an ``period`` option (see
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:py:meth:`~.BaseClusterDataModelCollector.get_config_opts`) that corresponds
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to the interval of time between each synchronization of the in-memory model.
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However, in more complex implementation, you may want to define some
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configuration options so one can tune the cluster data model collector to its
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needs. To do so, you can implement the :py:meth:`~.Loadable.get_config_opts`
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@ -73,8 +77,10 @@ class method as followed:
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# Do something here...
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return model
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def get_config_opts(self):
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return [
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@classmethod
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def get_config_opts(cls):
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return super(
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DummyClusterDataModelCollector, cls).get_config_opts() + [
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cfg.StrOpt('test_opt', help="Demo Option.", default=0),
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# Some more options ...
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]
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@ -91,8 +91,10 @@ tune the planner to its needs. To do so, you can implement the
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assert self.config.test_opt == 0
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# [...]
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def get_config_opts(self):
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return [
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@classmethod
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def get_config_opts(cls):
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return super(
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DummyPlanner, cls).get_config_opts() + [
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cfg.StrOpt('test_opt', help="Demo Option.", default=0),
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# Some more options ...
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]
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@ -0,0 +1,41 @@
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@startuml
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skinparam maxMessageSize 100
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actor "Administrator"
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== Initialization ==
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"Administrator" -> "Decision Engine" : Start all services
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"Decision Engine" -> "Background Task Scheduler" : Start
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activate "Background Task Scheduler"
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"Background Task Scheduler" -> "Cluster Model Collector Loader"\
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: List available cluster data models
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"Cluster Model Collector Loader" --> "Background Task Scheduler"\
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: list of BaseClusterModelCollector instances
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loop for every available cluster data model collector
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"Background Task Scheduler" -> "Background Task Scheduler"\
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: add periodic synchronization job
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create "Jobs Pool"
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"Background Task Scheduler" -> "Jobs Pool" : Create sync job
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end
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deactivate "Background Task Scheduler"
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hnote over "Background Task Scheduler" : Idle
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== Job workflow ==
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"Background Task Scheduler" -> "Jobs Pool" : Trigger synchronization job
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"Jobs Pool" -> "Nova Cluster Data Model Collector" : synchronize
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activate "Nova Cluster Data Model Collector"
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"Nova Cluster Data Model Collector" -> "Nova API"\
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: Fetch needed data to build the cluster data model
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"Nova API" --> "Nova Cluster Data Model Collector" : Needed data
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"Nova Cluster Data Model Collector" -> "Nova Cluster Data Model Collector"\
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: Build an in-memory cluster data model
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]o<-- "Nova Cluster Data Model Collector" : Done
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deactivate "Nova Cluster Data Model Collector"
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@enduml
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@ -10,45 +10,41 @@ activate "Decision Engine"
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"AMQP Bus" <[#blue]- "Decision Engine" : notify new audit state = ONGOING
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"Decision Engine" -> "Database" : get audit parameters (goal, strategy, ...)
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"Decision Engine" <-- "Database" : audit parameters (goal, strategy, ...)
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"Decision Engine" --> "Decision Engine": select appropriate \
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optimization strategy (via the Strategy Selector)
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"Decision Engine" --> "Decision Engine"\
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: select appropriate optimization strategy (via the Strategy Selector)
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create Strategy
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"Decision Engine" -> "Strategy" : execute()
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"Decision Engine" -> "Strategy" : execute strategy
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activate "Strategy"
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create "Cluster Data Model Collector"
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"Strategy" -> "Cluster Data Model Collector" : get cluster data model
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activate "Cluster Data Model Collector"
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loop while enough data to build cluster data model
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"Cluster Data Model Collector" -> "Nova API" : get resource state (\
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host, instance, ...)
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"Cluster Data Model Collector" <-- "Nova API" : resource state
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end
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"Cluster Data Model Collector" -> "Strategy" : cluster data model
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deactivate "Cluster Data Model Collector"
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"Cluster Data Model Collector" --> "Strategy"\
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: copy of the in-memory cluster data model
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loop while enough history data for the strategy
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"Strategy" -> "Ceilometer API" : get necessary metrics
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"Strategy" <-- "Ceilometer API" : aggregated metrics
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end
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"Strategy" -> "Strategy" : compute/set needed actions for the solution \
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so it achieves its goal
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"Strategy" -> "Strategy"\
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: compute/set needed actions for the solution so it achieves its goal
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"Strategy" -> "Strategy" : compute/set efficacy indicators for the solution
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"Strategy" -> "Strategy" : compute/set the solution global efficacy
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"Decision Engine" <-- "Strategy" : solution (contains a list of unordered \
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actions alongside its efficacy indicators as well as its global efficacy)
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"Decision Engine" <-- "Strategy"\
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: solution (unordered actions, efficacy indicators and global efficacy)
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deactivate "Strategy"
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"Decision Engine" --> "Planner": load actions scheduler (i.e. Planner plugin)
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create "Planner"
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"Decision Engine" -> "Planner": schedule()
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"Planner" -> "Planner": schedule actions according to \
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scheduling rules/policies
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"Decision Engine" -> "Planner" : load actions scheduler
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"Planner" --> "Decision Engine" : planner plugin
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"Decision Engine" -> "Planner" : schedule actions
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activate "Planner"
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"Planner" -> "Planner"\
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: schedule actions according to scheduling rules/policies
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"Decision Engine" <-- "Planner" : new action plan
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deactivate "Planner"
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"Decision Engine" -> "Database" : save new action plan in database
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"Decision Engine" -> "Database" : update audit.state = SUCCEEDED
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"AMQP Bus" <[#blue]- "Decision Engine" : notify new audit state = SUCCEEDED
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deactivate "Decision Engine"
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hnote over "Decision Engine" : Idle
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@enduml
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