def execute(self, orign_model):
current_model = orign_model
if orign_model is None:
raise exception.ClusterStateNotDefined()
migration = self.check_threshold(current_model)
LOG.warning(migration)
if migration:
hosts_load = self.get_hosts_load(current_model)
min_sd = 1
for vm_host in migration:
vm_load = self.calculate_migration_case(
hosts_load, vm_host['vm'], vm_host['s_host'],
vm_host['host'])
weighted_sd = self.calculate_weighted_sd(
self.metrics, vm_load[:-1])
if weighted_sd < min_sd:
min_sd = weighted_sd
hosts_load = vm_load[-1]
self.migrate(current_model, vm_host['vm'],
vm_host['s_host'], vm_host['host'])
for metric, value in zip(self.metrics, vm_load[:-1]):
if value < float(self.thresholds[metric]):
break
self.solution.model = current_model
self.solution.efficacy = 100
return self.solution
def calculate_score_vm(self, vm, cluster_data_model):
"""Calculate Score of virtual machine
:param vm: the virtual machine
:param cluster_data_model: the cluster model
:return: score
"""
if cluster_data_model is None:
raise exception.ClusterStateNotDefined()
vm_cpu_utilization = self.ceilometer. \
statistic_aggregation(
resource_id=vm.uuid,
meter_name=self.INSTANCE_CPU_USAGE_METRIC_NAME,
period="7200",
aggregate='avg'
)
if vm_cpu_utilization is None:
LOG.error(
_LE("No values returned by %(resource_id)s "
"for %(metric_name)s"),
resource_id=vm.uuid,
metric_name=self.INSTANCE_CPU_USAGE_METRIC_NAME,
)
vm_cpu_utilization = 100
cpu_capacity = cluster_data_model.get_resource_from_id(
resource.ResourceType.cpu_cores).get_capacity(vm)
total_cores_used = cpu_capacity * (vm_cpu_utilization / 100.0)
return self.calculate_weight(cluster_data_model, vm, total_cores_used,
0, 0)
def pre_execute(self):
LOG.debug("Initializing Uniform Airflow Strategy")
if not self.compute_model:
raise wexc.ClusterStateNotDefined()
LOG.debug(self.compute_model.to_string())
def pre_execute(self):
LOG.debug("Initializing Outlet temperature strategy")
if not self.compute_model:
raise wexc.ClusterStateNotDefined()
LOG.debug(self.compute_model.to_string())
def execute(self, original_model):
LOG.debug("Initializing Outlet temperature strategy")
if original_model is None:
raise wexc.ClusterStateNotDefined()
current_model = original_model
hosts_need_release, hosts_target = self.group_hosts_by_outlet_temp(
current_model)
if len(hosts_need_release) == 0:
# TODO(zhenzanz): return something right if there's no hot servers
LOG.debug("No hosts require optimization")
return self.solution
if len(hosts_target) == 0:
LOG.warning(_LE("No hosts under outlet temp threshold found"))
return self.solution
# choose the server with highest outlet t
hosts_need_release = sorted(hosts_need_release,
reverse=True,
key=lambda x: (x["outlet_temp"]))
vm_to_migrate = self.choose_vm_to_migrate(current_model,
hosts_need_release)
# calculate the vm's cpu cores,memory,disk needs
if vm_to_migrate is None:
return self.solution
mig_src_hypervisor, vm_src = vm_to_migrate
dest_servers = self.filter_dest_servers(current_model, hosts_target,
vm_src)
# sort the filtered result by outlet temp
# pick up the lowest one as dest server
if len(dest_servers) == 0:
# TODO(zhenzanz): maybe to warn that there's no resource
# for instance.
LOG.info(_LE("No proper target host could be found"))
return self.solution
dest_servers = sorted(dest_servers, key=lambda x: (x["outlet_temp"]))
# always use the host with lowerest outlet temperature
mig_dst_hypervisor = dest_servers[0]['hv']
# generate solution to migrate the vm to the dest server,
if current_model.get_mapping().migrate_vm(vm_src, mig_src_hypervisor,
mig_dst_hypervisor):
parameters = {
'migration_type': 'live',
'src_hypervisor': mig_src_hypervisor.uuid,
'dst_hypervisor': mig_dst_hypervisor.uuid
}
self.solution.add_action(action_type=self.MIGRATION,
resource_id=vm_src.uuid,
input_parameters=parameters)
self.solution.model = current_model
return self.solution
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())
def pre_execute(self):
LOG.info(_LI("Initializing Server Consolidation"))
if not self.compute_model:
raise exception.ClusterStateNotDefined()
if len(self.compute_model.get_all_compute_nodes()) == 0:
raise exception.ClusterEmpty()
LOG.debug(self.compute_model.to_string())
def pre_execute(self):
LOG.debug("Initializing Noisy Neighbor strategy")
if not self.compute_model:
raise wexc.ClusterStateNotDefined()
if self.compute_model.stale:
raise wexc.ClusterStateStale()
LOG.debug(self.compute_model.to_string())
def pre_execute(self):
"""Pre-execution phase
This can be used to fetch some pre-requisites or data.
"""
LOG.info(_LI("Initializing Workload Balance Strategy"))
if not self.compute_model:
raise wexc.ClusterStateNotDefined()
LOG.debug(self.compute_model.to_string())
def pre_execute(self):
LOG.info(_LI("Initializing Workload Stabilization"))
if not self.compute_model:
raise exception.ClusterStateNotDefined()
self.weights = self.input_parameters.weights
self.metrics = self.input_parameters.metrics
self.thresholds = self.input_parameters.thresholds
self.host_choice = self.input_parameters.host_choice
self.instance_metrics = self.input_parameters.instance_metrics
self.retry_count = self.input_parameters.retry_count
def pre_execute(self):
"""Pre-execution phase
This can be used to fetch some pre-requisites or data.
"""
LOG.info("Initializing Saving Energy Strategy")
if not self.compute_model:
raise wexc.ClusterStateNotDefined()
if self.compute_model.stale:
raise wexc.ClusterStateStale()
LOG.debug(self.compute_model.to_string())
def compute_model(self):
"""Cluster data model
:returns: Cluster data model the strategy is executed on
:rtype model: :py:class:`~.ModelRoot` instance
"""
if self._compute_model is None:
collector = self.collector_manager.get_cluster_model_collector(
'compute', osc=self.osc)
self._compute_model = self.audit_scope_handler.get_scoped_model(
collector.get_latest_cluster_data_model())
if not self._compute_model:
raise exception.ClusterStateNotDefined()
return self._compute_model
def _pre_execute(self):
"""Base Pre-execution phase
This will perform basic pre execution operations most strategies
should perform.
"""
LOG.info("Initializing " + self.get_display_name())
if not self.compute_model:
raise exception.ClusterStateNotDefined()
if self.compute_model.stale:
raise exception.ClusterStateStale()
LOG.debug(self.compute_model.to_string())
def execute(self, original_model):
LOG.info(_LI("Initializing Sercon Consolidation"))
if original_model is None:
raise exception.ClusterStateNotDefined()
# todo(jed) clone model
current_model = original_model
self.efficacy = 100
unsuccessful_migration = 0
first_migration = True
size_cluster = len(current_model.get_all_hypervisors())
if size_cluster == 0:
raise exception.ClusterEmpty()
self.compute_attempts(size_cluster)
for hypervisor_id in current_model.get_all_hypervisors():
hypervisor = current_model.get_hypervisor_from_id(hypervisor_id)
count = current_model.get_mapping(). \
get_node_vms_from_id(hypervisor_id)
if len(count) == 0:
if hypervisor.state == hyper_state.HypervisorState.ONLINE:
self.add_change_service_state(
hypervisor_id,
hyper_state.HypervisorState.OFFLINE.value)
while self.get_allowed_migration_attempts() >= unsuccessful_migration:
if not first_migration:
self.efficacy = self.calculate_migration_efficacy()
if self.efficacy < float(self.target_efficacy):
break
first_migration = False
score = []
score = self.score_of_nodes(current_model, score)
''' sort compute nodes by Score decreasing ''' ''
sorted_score = sorted(score, reverse=True, key=lambda x: (x[1]))
LOG.debug("Hypervisor(s) BFD {0}".format(sorted_score))
''' get Node to be released '''
if len(score) == 0:
LOG.warning(
_LW("The workloads of the compute nodes"
" of the cluster is zero"))
break
node_to_release, vm_score = self.node_and_vm_score(
sorted_score, score, current_model)
''' sort VMs by Score '''
sorted_vms = sorted(vm_score, reverse=True, key=lambda x: (x[1]))
# BFD: Best Fit Decrease
LOG.debug("VM(s) BFD {0}".format(sorted_vms))
migrations = self.calculate_num_migrations(sorted_vms,
current_model,
node_to_release,
sorted_score)
unsuccessful_migration = self.unsuccessful_migration_actualization(
migrations, unsuccessful_migration)
infos = {
"number_of_migrations": self.number_of_migrations,
"number_of_nodes_released": self.number_of_released_nodes,
"efficacy": self.efficacy
}
LOG.debug(infos)
self.solution.model = current_model
self.solution.efficacy = self.efficacy
return self.solution
