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Update Overload standard deviation doc

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Bug #2113862 details a number of suggested
corrections and additions to the Workload
Stabilization doc. This patch adds those
suggested changes.

Closes-Bug: #2113862
Assisted-By: Cursor (claude-3.5-sonnet)
Change-Id: I4131a304c064d2ea397b2447025c7edf69a56e2a
Signed-off-by: Ronelle Landy <rlandy@redhat.com>
This commit is contained in:
Ronelle Landy
2025-07-03 16:51:09 -04:00
parent 6d155c4be6
commit 457819072f
3 changed files with 108 additions and 48 deletions
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16
watcher/decision_engine/strategy/strategies/workload_stabilization.py
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@@ -48,9 +48,19 @@ def _set_memoize(conf):
class WorkloadStabilization(base.WorkloadStabilizationBaseStrategy):
"""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.
This workload stabilization strategy is based on the standard deviation
algorithm, as a measure of cluster resource usage balance. The goal is to
determine if there is an overload in a cluster and respond to it by
migrating VMs to stabilize the cluster.
The standard deviation is determined using normalized CPU and/or memory
usage values, which are scaled to a range between 0 and 1 based on the
usage metrics in the data sources.
A standard deviation of 0 means that your cluster's resources are
perfectly balanced, with all usage values being identical. However, a
standard deviation of 0.5 indicates completely unbalanced resource usage,
where some resources are heavily utilized and others are not at all.
This strategy has been tested in a small (32 nodes) cluster.