59c5adc8ad
Here is a new architecture diagram with some updates on the glossary and on descriptions of architecture elements. I also fix some warning logs. Closes-Bug: #1657405 Change-Id: I442082d702fc8667e9397c090da51ca1ead5d86e
234 lines
8.6 KiB
Python
234 lines
8.6 KiB
Python
# -*- encoding: utf-8 -*-
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#
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# Authors: Vincent Francoise <Vincent.FRANCOISE@b-com.com>
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# Alexander Chadin <a.chadin@servionica.ru>
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import networkx as nx
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from oslo_config import cfg
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from oslo_config import types
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from oslo_log import log
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from watcher._i18n import _LW
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from watcher.common import utils
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from watcher.decision_engine.planner import base
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from watcher import objects
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LOG = log.getLogger(__name__)
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class WeightPlanner(base.BasePlanner):
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"""Weight planner implementation
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This implementation builds actions with parents in accordance with weights.
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Set of actions having a lower weight will be scheduled before
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the other ones. There are two config options to configure:
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action_weights and parallelization.
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*Limitations*
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- This planner requires to have action_weights and parallelization configs
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tuned well.
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"""
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def __init__(self, config):
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super(WeightPlanner, self).__init__(config)
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action_weights = {
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'turn_host_to_acpi_s3_state': 10,
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'resize': 20,
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'migrate': 30,
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'sleep': 40,
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'change_nova_service_state': 50,
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'nop': 60,
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}
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parallelization = {
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'turn_host_to_acpi_s3_state': 2,
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'resize': 2,
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'migrate': 2,
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'sleep': 1,
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'change_nova_service_state': 1,
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'nop': 1,
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}
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@classmethod
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def get_config_opts(cls):
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return [
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cfg.Opt(
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'weights',
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type=types.Dict(value_type=types.Integer()),
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help="These weights are used to schedule the actions. "
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"Action Plan will be build in accordance with sets of "
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"actions ordered by descending weights."
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"Two action types cannot have the same weight. ",
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default=cls.action_weights),
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cfg.Opt(
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'parallelization',
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type=types.Dict(value_type=types.Integer()),
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help="Number of actions to be run in parallel on a per "
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"action type basis.",
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default=cls.parallelization),
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]
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@staticmethod
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def format_action(action_plan_id, action_type,
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input_parameters=None, parents=()):
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return {
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'uuid': utils.generate_uuid(),
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'action_plan_id': int(action_plan_id),
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'action_type': action_type,
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'input_parameters': input_parameters,
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'state': objects.action.State.PENDING,
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'parents': parents or None,
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}
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@staticmethod
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def chunkify(lst, n):
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"""Yield successive n-sized chunks from lst."""
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if n < 1:
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# Just to make sure the number is valid
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n = 1
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# Split a flat list in a list of chunks of size n.
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# e.g. chunkify([0, 1, 2, 3, 4], 2) -> [[0, 1], [2, 3], [4]]
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for i in range(0, len(lst), n):
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yield lst[i:i + n]
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def compute_action_graph(self, sorted_weighted_actions):
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reverse_weights = {v: k for k, v in self.config.weights.items()}
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# leaf_groups contains a list of list of nodes called groups
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# each group is a set of nodes from which a future node will
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# branch off (parent nodes).
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# START --> migrate-1 --> migrate-3
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# \ \--> resize-1 --> FINISH
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# \--> migrate-2 -------------/
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# In the above case migrate-1 will the only memeber of the leaf
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# group that migrate-3 will use as parent group, whereas
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# resize-1 will have both migrate-2 and migrate-3 in its
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# parent/leaf group
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leaf_groups = []
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action_graph = nx.DiGraph()
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# We iterate through each action type category (sorted by weight) to
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# insert them in a Directed Acyclic Graph
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for idx, (weight, actions) in enumerate(sorted_weighted_actions):
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action_chunks = self.chunkify(
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actions, self.config.parallelization[reverse_weights[weight]])
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# We split the actions into chunks/layers that will have to be
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# spread across all the available branches of the graph
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for chunk_idx, actions_chunk in enumerate(action_chunks):
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for action in actions_chunk:
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action_graph.add_node(action)
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# all other actions
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parent_nodes = []
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if not idx and not chunk_idx:
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parent_nodes = []
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elif leaf_groups:
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parent_nodes = leaf_groups
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for parent_node in parent_nodes:
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action_graph.add_edge(parent_node, action)
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action.parents.append(parent_node.uuid)
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if leaf_groups:
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leaf_groups = []
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leaf_groups.extend([a for a in actions_chunk])
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return action_graph
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def schedule(self, context, audit_id, solution):
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LOG.debug('Creating an action plan for the audit uuid: %s', audit_id)
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action_plan = self.create_action_plan(context, audit_id, solution)
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sorted_weighted_actions = self.get_sorted_actions_by_weight(
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context, action_plan, solution)
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action_graph = self.compute_action_graph(sorted_weighted_actions)
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self._create_efficacy_indicators(
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context, action_plan.id, solution.efficacy_indicators)
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if len(action_graph.nodes()) == 0:
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LOG.warning(_LW("The action plan is empty"))
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action_plan.state = objects.action_plan.State.SUCCEEDED
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action_plan.save()
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self.create_scheduled_actions(action_plan, action_graph)
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return action_plan
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def get_sorted_actions_by_weight(self, context, action_plan, solution):
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# We need to make them immutable to add them to the graph
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action_objects = list([
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objects.Action(
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context, uuid=utils.generate_uuid(), parents=[],
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action_plan_id=action_plan.id, **a)
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for a in solution.actions])
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# This is a dict of list with each being a weight and the list being
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# all the actions associated to this weight
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weighted_actions = collections.defaultdict(list)
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for action in action_objects:
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action_weight = self.config.weights[action.action_type]
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weighted_actions[action_weight].append(action)
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return reversed(sorted(weighted_actions.items(), key=lambda x: x[0]))
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def create_scheduled_actions(self, action_plan, graph):
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for action in graph.nodes():
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LOG.debug("Creating the %s in the Watcher database",
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action.action_type)
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try:
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action.create()
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except Exception as exc:
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LOG.exception(exc)
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raise
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def create_action_plan(self, context, audit_id, solution):
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strategy = objects.Strategy.get_by_name(
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context, solution.strategy.name)
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action_plan_dict = {
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'uuid': utils.generate_uuid(),
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'audit_id': audit_id,
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'strategy_id': strategy.id,
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'state': objects.action_plan.State.RECOMMENDED,
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'global_efficacy': solution.global_efficacy,
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}
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new_action_plan = objects.ActionPlan(context, **action_plan_dict)
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new_action_plan.create()
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return new_action_plan
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def _create_efficacy_indicators(self, context, action_plan_id, indicators):
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efficacy_indicators = []
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for indicator in indicators:
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efficacy_indicator_dict = {
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'uuid': utils.generate_uuid(),
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'name': indicator.name,
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'description': indicator.description,
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'unit': indicator.unit,
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'value': indicator.value,
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'action_plan_id': action_plan_id,
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}
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new_efficacy_indicator = objects.EfficacyIndicator(
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context, **efficacy_indicator_dict)
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new_efficacy_indicator.create()
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efficacy_indicators.append(new_efficacy_indicator)
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return efficacy_indicators
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