Watcher Overload standard deviation algorithm

updated: 2017-06-30 18:01

Watcher Overload standard deviation algorithm

Watcher Overload standard deviation algorithm¶

Synopsis¶

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.

Requirements¶

Metrics¶

The workload_stabilization strategy requires the following metrics:

metric	service name	plugins
`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.

Actions¶

Default Watcher’s actions:

action description
migration
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).

action	description
`migration`	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`).

Planner¶

Default Watcher’s planner:

Configuration¶

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 recieved 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.'}

Algorithm¶

You can find description of overload algorithm and role of standard deviation here: https://specs.openstack.org/openstack/watcher-specs/specs/newton/implemented/sd-strategy.html

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"]'

External Links¶

Watcher Overload standard deviation algorithm spec