Feature Overview

RFE: https://issues.redhat.com/browse/RFE-8626 

This feature aims to productize the upstream Dynamic Scoring Framework add-on for Open Cluster Management (OCM) into Red Hat Advanced Cluster Management (RHACM). It introduces a mechanism to make Placement decisions based on custom, external logic (Scoring API) and real-time time-series data, moving beyond static labels or simple resource claims.

Technical Preview scope of support: https://access.redhat.com/support/offerings/techpreview

Goals (GA)

• Intelligent Resource Efficiency: Place workloads based on real-time utilization (e.g., GPU/CPU power load) rather than just static capacity. 

• Predictive Orchestration: Enable "Smart Scheduling" by integrating with AI/LLMs and predictive agents to target clusters based on forecasted utilization. 

• Ecosystem Integration: Allow placement decisions to be informed by Red Hat-specific data sources, such as Red Hat Lightspeed (Insights) or OpenShift AI risk and compliance scores. 

• Performance at Scale: Maintain high data freshness for scheduling without overwhelming the Hub cluster's performance by processing metrics locally on managed clusters.

Requirements

This Section: A list of specific needs or objectives that a Feature must
deliver to satisfy the Feature.. Some requirements will be flagged as MVP.
If an MVP gets shifted, the feature shifts. If a non MVP requirement slips,
it does not shift the feature.

Some Use Cases (for GA, not DP)

• AI Workload Bursting: A customer uses a custom Scoring API to monitor real-time GPU power load predictions; the framework automatically relocates AI workloads to clusters forecasted to have the highest efficiency. 

• Compliance-Driven Placement: The Scoring API fetches a "Risk Score" from Red Hat Insights. OCM then restricts sensitive workloads to clusters currently meeting the highest compliance threshold. 

• Cost-Aware Scheduling: Placement scores are dynamically adjusted based on external factors like variable electricity rates or operational hardware efficiency.

• (Stretch) Natural Language Querying: An administrator uses a chatbot integrated via an MCP server to ask, "Which cluster is currently the most cost-effective for a high-memory job?" and the system provides a recommendation based on live dynamic scores.

Questions to answer

• ...

Out of Scope

• The customer brings the scoring engine so we won't create one at this time. 
• Connections to external tools.

Background, and strategic fit

This Section: What does the person writing code, testing, documenting
need to know? What context can be provided to frame this feature?

Assumptions

• ...

Customer Considerations

• ...

Documentation Considerations

Questions to be addressed:

• What educational or reference material (docs) is required to support this
  product feature? For users/admins? Other functions (security officers, etc)?
• Does this feature have a doc impact?
• New Content, Updates to existing content, Release Note, or No Doc Impact
• If unsure and no Technical Writer is available, please contact Content
  Strategy.
• What concepts do customers need to understand to be successful in
  [action]?
• How do we expect customers will use the feature? For what purpose(s)?
• What reference material might a customer want/need to complete [action]?
• Is there source material that can be used as reference for the Technical
  Writer in writing the content? If yes, please link if available.
• What is the doc impact (New Content, Updates to existing content, or
  Release Note)?