Watcher Overload standard deviation algorithm

Watcher Overload standard deviation algorithm


display name: Workload stabilization

goal: workload_balancing

Workload Stabilization control using live migration

This is workload stabilization strategy based on standard deviation algorithm. The goal is to determine if there is an overload in a cluster and respond to it by migrating VMs to stabilize the cluster.

It assumes that live migrations are possible in your cluster.



The workload_stabilization strategy requires the following metrics:

metric service name plugins comment
compute.node.cpu.percent ceilometer none  
hardware.memory.used ceilometer SNMP  
cpu_util ceilometer none  
memory.resident ceilometer none  

Cluster data model

Default Watcher’s Compute cluster data model:

Nova cluster data model collector

The Nova cluster data model collector creates an in-memory representation of the resources exposed by the compute service.


Default Watcher’s actions:

action description

Migrates a server to a destination nova-compute host

This action will allow you to migrate a server to another compute destination host. Migration type ‘live’ can only be used for migrating active VMs. Migration type ‘cold’ can be used for migrating non-active VMs as well active VMs, which will be shut down while migrating.

The action schema is:

schema = Schema({
 'resource_id': str,  # should be a UUID
 'migration_type': str,  # choices -> "live", "cold"
 'destination_node': str,
 'source_node': str,

The resource_id is the UUID of the server to migrate. The source_node and destination_node parameters are respectively the source and the destination compute hostname (list of available compute hosts is returned by this command: nova service-list --binary nova-compute).


Default Watcher’s planner:

Weight planner implementation

This implementation builds actions with parents in accordance with weights. Set of actions having a higher weight will be scheduled before the other ones. There are two config options to configure: action_weights and parallelization.


  • This planner requires to have action_weights and parallelization configs tuned well.


Strategy parameters are:

parameter type default Value description
metrics array [“cpu_util”, “memory.resident”] Metrics used as rates of cluster loads.
thresholds object {“cpu_util”: 0.2, “memory.resident”: 0.2} Dict where key is a metric and value is a trigger value.
weights object {“cpu_util_weight”: 1.0, “memory.resident_weight”: 1.0} These weights used to calculate common standard deviation. Name of weight contains meter name and _weight suffix.
instance_metrics object {“cpu_util”: “compute.node.cpu.percent”, “memory.resident”: “hardware.memory.used”} Mapping to get hardware statistics using instance metrics.
host_choice string retry Method of host’s choice. There are cycle, retry and fullsearch methods. Cycle will iterate hosts in cycle. Retry will get some hosts random (count defined in retry_count option). Fullsearch will return each host from list.
retry_count number 1 Count of random returned hosts.
periods object {“instance”: 720, “node”: 600} These periods are used to get statistic aggregation for instance and host metrics. The period is simply a repeating interval of time into which the samples are grouped for aggregation. Watcher uses only the last period of all received ones.

Efficacy Indicator

[{'value': 0, 'name': 'released_nodes_ratio', 'unit': '%', 'description': u'Ratio of released compute nodes divided by the total number of enabled compute nodes.'}]


You can find description of overload algorithm and role of standard deviation here:

How to use it ?

$ openstack optimize audittemplate create \
  at1 workload_balancing --strategy workload_stabilization

$ openstack optimize audit create -a at1 \
  -p thresholds='{"memory.resident": 0.05}' \
  -p metrics='["memory.resident"]'
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