Value Statement

As a cluster admin, I can analyze CPU, memory, and power consumption metrics for federated learning instances across clusters through AIOps, leveraging agentic workflows for visualization and insights.

Definition of Done for Engineering Story Owner (Checklist)

Acceptance Criteria

1. Collect CPU, memory, and power consumption metrics from federated learning server and clients.

1. Display metrics in real time and allow historical analysis.

1. Use agentic workflows to process, correlate, and visualize metrics.

1. Enable filtering and comparison of metrics across clusters.

Development Complete

• The code is complete.
• Functionality is working.
• Any required downstream Docker file changes are made.

Tests Automated

• [ ] Unit/function tests have been automated and incorporated into the
  build.
• [ ] 100% automated unit/function test coverage for new or changed APIs.

Secure Design

• [ ] Security has been assessed and incorporated into your threat model.

Multidisciplinary Teams Readiness

• [ ] Create an informative documentation issue using the [Customer
  Portal_doc_issue template](
  https://github.com/stolostron/backlog/issues/new?assignees=&labels=squad%3Adoc&template=doc_issue.md&title=),
  and ensure doc acceptance criteria is met. Link the development issue to
  the doc issue.
• [ ] Provide input to the QE team, and ensure QE acceptance criteria
  (established between story owner and QE focal) are met.

Support Readiness

• [ ] The must-gather script has been updated.