Epic Goal

• to compare the performance of the apiserver and the scheduling of pods in clusters with the Multiarch Tuning Operator (MTO) installed with a ClusterPodPlacementConfig and clusters with no MTO.
• to discover bugs among the scheduler, kube-apiserver (in the scheduling gates scenarios), and the operator, by testing at scale
• to address minor performance optimizations in the operator

Why is this important?

• The operator affects a very sensitive aspect of the lifecycle of a pod: in between the creation and the persistence of the object in etcd, and before the pod enters the scheduling queue.

Scenarios
1. …

Acceptance Criteria

• Test results are delivered and outcomes are evaluated

Dependencies (internal and external)
1. …

Previous Work (Optional):
1. …

Open questions::
1. …

Done Checklist

• DEV - To support perf-scale testers with design details, metrics and ad-hoc implementations to support the comparitve evaluation
• QE - Test plans in Test Plan tracking software (e.g. Polarion, RQM, etc.)
• QE - Tests executed and evaluated
• All the stories, tasks, sub-tasks and bugs that belong to this epic need to have been completed and indicated by a status of 'Done'.