watcher.decision_engine.strategy.strategies.vm_workload_consolidation

Source code for watcher.decision_engine.strategy.strategies.vm_workload_consolidation

# -*- encoding: utf-8 -*-
#
# Authors: Vojtech CIMA <cima@zhaw.ch>
#          Bruno GRAZIOLI <gaea@zhaw.ch>
#          Sean MURPHY <murp@zhaw.ch>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

from oslo_config import cfg
from oslo_log import log
import six

from watcher._i18n import _
from watcher.common import exception
from watcher.decision_engine.model import element
from watcher.decision_engine.strategy.strategies import base

LOG = log.getLogger(__name__)


[docs]class VMWorkloadConsolidation(base.ServerConsolidationBaseStrategy): """VM Workload Consolidation Strategy A load consolidation strategy based on heuristic first-fit algorithm which focuses on measured CPU utilization and tries to minimize hosts which have too much or too little load respecting resource capacity constraints. This strategy produces a solution resulting in more efficient utilization of cluster resources using following four phases: * Offload phase - handling over-utilized resources * Consolidation phase - handling under-utilized resources * Solution optimization - reducing number of migrations * Disability of unused compute nodes A capacity coefficients (cc) might be used to adjust optimization thresholds. Different resources may require different coefficient values as well as setting up different coefficient values in both phases may lead to to more efficient consolidation in the end. If the cc equals 1 the full resource capacity may be used, cc values lower than 1 will lead to resource under utilization and values higher than 1 will lead to resource overbooking. e.g. If targeted utilization is 80 percent of a compute node capacity, the coefficient in the consolidation phase will be 0.8, but may any lower value in the offloading phase. The lower it gets the cluster will appear more released (distributed) for the following consolidation phase. As this strategy leverages VM live migration to move the load from one compute node to another, this feature needs to be set up correctly on all compute nodes within the cluster. This strategy assumes it is possible to live migrate any VM from an active compute node to any other active compute node. """ HOST_CPU_USAGE_METRIC_NAME = 'compute.node.cpu.percent' INSTANCE_CPU_USAGE_METRIC_NAME = 'cpu_util' DATASOURCE_METRICS = ['instance_ram_allocated', 'instance_cpu_usage', 'instance_ram_usage', 'instance_root_disk_size'] METRIC_NAMES = dict( ceilometer=dict( cpu_util_metric='cpu_util', ram_util_metric='memory.resident', ram_alloc_metric='memory', disk_alloc_metric='disk.root.size'), gnocchi=dict( cpu_util_metric='cpu_util', ram_util_metric='memory.resident', ram_alloc_metric='memory', disk_alloc_metric='disk.root.size'), ) MIGRATION = "migrate" CHANGE_NOVA_SERVICE_STATE = "change_nova_service_state" def __init__(self, config, osc=None): super(VMWorkloadConsolidation, self).__init__(config, osc) self._ceilometer = None self._gnocchi = None self.number_of_migrations = 0 self.number_of_released_nodes = 0 # self.ceilometer_instance_data_cache = dict() self.datasource_instance_data_cache = dict()
[docs] @classmethod def get_name(cls): return "vm_workload_consolidation"
[docs] @classmethod def get_display_name(cls): return _("VM Workload Consolidation Strategy")
[docs] @classmethod def get_translatable_display_name(cls): return "VM Workload Consolidation Strategy"
@property def period(self): return self.input_parameters.get('period', 3600) @property def granularity(self): return self.input_parameters.get('granularity', 300)
[docs] @classmethod def get_schema(cls): # Mandatory default setting for each element return { "properties": { "period": { "description": "The time interval in seconds for " "getting statistic aggregation", "type": "number", "default": 3600 }, "granularity": { "description": "The time between two measures in an " "aggregated timeseries of a metric.", "type": "number", "default": 300 }, } }
[docs] @classmethod def get_config_opts(cls): return [ cfg.StrOpt( "datasource", help="Data source to use in order to query the needed metrics", default="gnocchi", choices=["ceilometer", "gnocchi"]) ]
[docs] def get_available_compute_nodes(self): default_node_scope = [element.ServiceState.ENABLED.value, element.ServiceState.DISABLED.value] return {uuid: cn for uuid, cn in self.compute_model.get_all_compute_nodes().items() if cn.state == element.ServiceState.ONLINE.value and cn.status in default_node_scope}
[docs] def get_instance_state_str(self, instance): """Get instance state in string format. :param instance: """ if isinstance(instance.state, six.string_types): return instance.state elif isinstance(instance.state, element.InstanceState): return instance.state.value else: LOG.error('Unexpected instance state type, ' 'state=%(state)s, state_type=%(st)s.', dict(state=instance.state, st=type(instance.state))) raise exception.WatcherException
[docs] def get_node_status_str(self, node): """Get node status in string format. :param node: """ if isinstance(node.status, six.string_types): return node.status elif isinstance(node.status, element.ServiceState): return node.status.value else: LOG.error('Unexpected node status type, ' 'status=%(status)s, status_type=%(st)s.', dict(status=node.status, st=type(node.status))) raise exception.WatcherException
[docs] def add_action_enable_compute_node(self, node): """Add an action for node enabler into the solution. :param node: node object :return: None """ params = {'state': element.ServiceState.ENABLED.value} self.solution.add_action( action_type=self.CHANGE_NOVA_SERVICE_STATE, resource_id=node.uuid, input_parameters=params) self.number_of_released_nodes -= 1
[docs] def add_action_disable_node(self, node): """Add an action for node disability into the solution. :param node: node object :return: None """ params = {'state': element.ServiceState.DISABLED.value, 'disabled_reason': self.REASON_FOR_DISABLE} self.solution.add_action( action_type=self.CHANGE_NOVA_SERVICE_STATE, resource_id=node.uuid, input_parameters=params) self.number_of_released_nodes += 1
[docs] def add_migration(self, instance, source_node, destination_node): """Add an action for VM migration into the solution. :param instance: instance object :param source_node: node object :param destination_node: node object :return: None """ instance_state_str = self.get_instance_state_str(instance) if instance_state_str not in (element.InstanceState.ACTIVE.value, element.InstanceState.PAUSED.value): # Watcher currently only supports live VM migration and block live # VM migration which both requires migrated VM to be active. # When supported, the cold migration may be used as a fallback # migration mechanism to move non active VMs. LOG.error( 'Cannot live migrate: instance_uuid=%(instance_uuid)s, ' 'state=%(instance_state)s.', dict( instance_uuid=instance.uuid, instance_state=instance_state_str)) return migration_type = 'live' # Here will makes repeated actions to enable the same compute node, # when migrating VMs to the destination node which is disabled. # Whether should we remove the same actions in the solution??? destination_node_status_str = self.get_node_status_str( destination_node) if destination_node_status_str == element.ServiceState.DISABLED.value: self.add_action_enable_compute_node(destination_node) if self.compute_model.migrate_instance( instance, source_node, destination_node): params = {'migration_type': migration_type, 'source_node': source_node.uuid, 'destination_node': destination_node.uuid} self.solution.add_action(action_type=self.MIGRATION, resource_id=instance.uuid, input_parameters=params) self.number_of_migrations += 1
[docs] def disable_unused_nodes(self): """Generate actions for disabling unused nodes. :return: None """ for node in self.get_available_compute_nodes().values(): if (len(self.compute_model.get_node_instances(node)) == 0 and node.status != element.ServiceState.DISABLED.value): self.add_action_disable_node(node)
[docs] def get_instance_utilization(self, instance): """Collect cpu, ram and disk utilization statistics of a VM. :param instance: instance object :param aggr: string :return: dict(cpu(number of vcpus used), ram(MB used), disk(B used)) """ instance_cpu_util = None instance_ram_util = None instance_disk_util = None if instance.uuid in self.datasource_instance_data_cache.keys(): return self.datasource_instance_data_cache.get(instance.uuid) cpu_util_metric = self.METRIC_NAMES[ self.config.datasource]['cpu_util_metric'] ram_util_metric = self.METRIC_NAMES[ self.config.datasource]['ram_util_metric'] ram_alloc_metric = self.METRIC_NAMES[ self.config.datasource]['ram_alloc_metric'] disk_alloc_metric = self.METRIC_NAMES[ self.config.datasource]['disk_alloc_metric'] instance_cpu_util = self.datasource_backend.statistic_aggregation( resource_id=instance.uuid, meter_name=cpu_util_metric, period=self.period, granularity=self.granularity) instance_ram_util = self.datasource_backend.statistic_aggregation( resource_id=instance.uuid, meter_name=ram_util_metric, period=self.period, granularity=self.granularity) if not instance_ram_util: instance_ram_util = self.datasource_backend.statistic_aggregation( resource_id=instance.uuid, meter_name=ram_alloc_metric, period=self.period, granularity=self.granularity) instance_disk_util = self.datasource_backend.statistic_aggregation( resource_id=instance.uuid, meter_name=disk_alloc_metric, period=self.period, granularity=self.granularity) if instance_cpu_util: total_cpu_utilization = ( instance.vcpus * (instance_cpu_util / 100.0)) else: total_cpu_utilization = instance.vcpus if not instance_ram_util: instance_ram_util = instance.memory LOG.warning('No values returned by %s for memory.resident, ' 'use instance flavor ram value', instance.uuid) if not instance_disk_util: instance_disk_util = instance.disk LOG.warning('No values returned by %s for disk.root.size, ' 'use instance flavor disk value', instance.uuid) self.datasource_instance_data_cache[instance.uuid] = dict( cpu=total_cpu_utilization, ram=instance_ram_util, disk=instance_disk_util) return self.datasource_instance_data_cache.get(instance.uuid)
[docs] def get_node_utilization(self, node): """Collect cpu, ram and disk utilization statistics of a node. :param node: node object :param aggr: string :return: dict(cpu(number of cores used), ram(MB used), disk(B used)) """ node_instances = self.compute_model.get_node_instances(node) node_ram_util = 0 node_disk_util = 0 node_cpu_util = 0 for instance in node_instances: instance_util = self.get_instance_utilization( instance) node_cpu_util += instance_util['cpu'] node_ram_util += instance_util['ram'] node_disk_util += instance_util['disk'] return dict(cpu=node_cpu_util, ram=node_ram_util, disk=node_disk_util)
[docs] def get_node_capacity(self, node): """Collect cpu, ram and disk capacity of a node. :param node: node object :return: dict(cpu(cores), ram(MB), disk(B)) """ return dict(cpu=node.vcpus, ram=node.memory, disk=node.disk_capacity)
[docs] def get_relative_node_utilization(self, node): """Return relative node utilization. :param node: node object :return: {'cpu': <0,1>, 'ram': <0,1>, 'disk': <0,1>} """ relative_node_utilization = {} util = self.get_node_utilization(node) cap = self.get_node_capacity(node) for k in util.keys(): relative_node_utilization[k] = float(util[k]) / float(cap[k]) return relative_node_utilization
[docs] def get_relative_cluster_utilization(self): """Calculate relative cluster utilization (rcu). RCU is an average of relative utilizations (rhu) of active nodes. :return: {'cpu': <0,1>, 'ram': <0,1>, 'disk': <0,1>} """ nodes = self.get_available_compute_nodes().values() rcu = {} counters = {} for node in nodes: node_status_str = self.get_node_status_str(node) if node_status_str == element.ServiceState.ENABLED.value: rhu = self.get_relative_node_utilization(node) for k in rhu.keys(): if k not in rcu: rcu[k] = 0 if k not in counters: counters[k] = 0 rcu[k] += rhu[k] counters[k] += 1 for k in rcu.keys(): rcu[k] /= counters[k] return rcu
[docs] def is_overloaded(self, node, cc): """Indicate whether a node is overloaded. This considers provided resource capacity coefficients (cc). :param node: node object :param cc: dictionary containing resource capacity coefficients :return: [True, False] """ node_capacity = self.get_node_capacity(node) node_utilization = self.get_node_utilization( node) metrics = ['cpu'] for m in metrics: if node_utilization[m] > node_capacity[m] * cc[m]: return True return False
[docs] def instance_fits(self, instance, node, cc): """Indicate whether is a node able to accommodate a VM. This considers provided resource capacity coefficients (cc). :param instance: :py:class:`~.element.Instance` :param node: node object :param cc: dictionary containing resource capacity coefficients :return: [True, False] """ node_capacity = self.get_node_capacity(node) node_utilization = self.get_node_utilization(node) instance_utilization = self.get_instance_utilization(instance) metrics = ['cpu', 'ram', 'disk'] for m in metrics: if (instance_utilization[m] + node_utilization[m] > node_capacity[m] * cc[m]): return False return True
[docs] def optimize_solution(self): """Optimize solution. This is done by eliminating unnecessary or circular set of migrations which can be replaced by a more efficient solution. e.g.: * A->B, B->C => replace migrations A->B, B->C with a single migration A->C as both solution result in VM running on node C which can be achieved with one migration instead of two. * A->B, B->A => remove A->B and B->A as they do not result in a new VM placement. """ migrate_actions = ( a for a in self.solution.actions if a[ 'action_type'] == self.MIGRATION) instance_to_be_migrated = ( a['input_parameters']['resource_id'] for a in migrate_actions) instance_uuids = list(set(instance_to_be_migrated)) for instance_uuid in instance_uuids: actions = list( a for a in self.solution.actions if a[ 'input_parameters'][ 'resource_id'] == instance_uuid) if len(actions) > 1: src_uuid = actions[0]['input_parameters']['source_node'] dst_uuid = actions[-1]['input_parameters']['destination_node'] for a in actions: self.solution.actions.remove(a) self.number_of_migrations -= 1 src_node = self.compute_model.get_node_by_uuid(src_uuid) dst_node = self.compute_model.get_node_by_uuid(dst_uuid) instance = self.compute_model.get_instance_by_uuid( instance_uuid) if self.compute_model.migrate_instance( instance, dst_node, src_node): self.add_migration(instance, src_node, dst_node)
[docs] def offload_phase(self, cc): """Perform offloading phase. This considers provided resource capacity coefficients. Offload phase performing first-fit based bin packing to offload overloaded nodes. This is done in a fashion of moving the least CPU utilized VM first as live migration these generally causes less troubles. This phase results in a cluster with no overloaded nodes. * This phase is be able to enable disabled nodes (if needed and any available) in the case of the resource capacity provided by active nodes is not able to accommodate all the load. As the offload phase is later followed by the consolidation phase, the node enabler in this phase doesn't necessarily results in more enabled nodes in the final solution. :param cc: dictionary containing resource capacity coefficients """ sorted_nodes = sorted( self.get_available_compute_nodes().values(), key=lambda x: self.get_node_utilization(x)['cpu']) for node in reversed(sorted_nodes): if self.is_overloaded(node, cc): for instance in sorted( self.compute_model.get_node_instances(node), key=lambda x: self.get_instance_utilization( x)['cpu'] ): # skip exclude instance when migrating if instance.watcher_exclude: LOG.debug("Instance is excluded by scope, " "skipped: %s", instance.uuid) continue for destination_node in reversed(sorted_nodes): if self.instance_fits( instance, destination_node, cc): self.add_migration(instance, node, destination_node) break if not self.is_overloaded(node, cc): break
[docs] def consolidation_phase(self, cc): """Perform consolidation phase. This considers provided resource capacity coefficients. Consolidation phase performing first-fit based bin packing. First, nodes with the lowest cpu utilization are consolidated by moving their load to nodes with the highest cpu utilization which can accommodate the load. In this phase the most cpu utilized VMs are prioritized as their load is more difficult to accommodate in the system than less cpu utilized VMs which can be later used to fill smaller CPU capacity gaps. :param cc: dictionary containing resource capacity coefficients """ sorted_nodes = sorted( self.get_available_compute_nodes().values(), key=lambda x: self.get_node_utilization(x)['cpu']) asc = 0 for node in sorted_nodes: instances = sorted( self.compute_model.get_node_instances(node), key=lambda x: self.get_instance_utilization(x)['cpu']) for instance in reversed(instances): # skip exclude instance when migrating if instance.watcher_exclude: LOG.debug("Instance is excluded by scope, " "skipped: %s", instance.uuid) continue dsc = len(sorted_nodes) - 1 for destination_node in reversed(sorted_nodes): if asc >= dsc: break if self.instance_fits( instance, destination_node, cc): self.add_migration(instance, node, destination_node) break dsc -= 1 asc += 1
[docs] def pre_execute(self): if not self.compute_model: raise exception.ClusterStateNotDefined() if self.compute_model.stale: raise exception.ClusterStateStale() LOG.debug(self.compute_model.to_string())
[docs] def do_execute(self): """Execute strategy. This strategy produces a solution resulting in more efficient utilization of cluster resources using following four phases: * Offload phase - handling over-utilized resources * Consolidation phase - handling under-utilized resources * Solution optimization - reducing number of migrations * Disability of unused nodes :param original_model: root_model object """ LOG.info('Executing Smart Strategy') rcu = self.get_relative_cluster_utilization() cc = {'cpu': 1.0, 'ram': 1.0, 'disk': 1.0} # Offloading phase self.offload_phase(cc) # Consolidation phase self.consolidation_phase(cc) # Optimize solution self.optimize_solution() # disable unused nodes self.disable_unused_nodes() rcu_after = self.get_relative_cluster_utilization() info = { "compute_nodes_count": len( self.get_available_compute_nodes()), 'number_of_migrations': self.number_of_migrations, 'number_of_released_nodes': self.number_of_released_nodes, 'relative_cluster_utilization_before': str(rcu), 'relative_cluster_utilization_after': str(rcu_after) } LOG.debug(info)
[docs] def post_execute(self): self.solution.set_efficacy_indicators( compute_nodes_count=len( self.get_available_compute_nodes()), released_compute_nodes_count=self.number_of_released_nodes, instance_migrations_count=self.number_of_migrations, ) LOG.debug(self.compute_model.to_string())
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