AI4Canvas Operator

[emmanuel-a-otchere]

Description

AI4Canvas Operator is an ODA Canvas Operator that leverages artificial intelligence (AI) to optimize Canvas resource allocation and workload management for ODA Components. Primary purpose - enhance ODA Component operations efficiency, scalability, and resource utilization within the canvas. It is meant to enable AI and ODA Component management.

Key Objectives

1. AI-Based ODA Component Workload Prediction:

   • The AI4Canvas Operator employs machine learning and signal processing techniques to predict resource usage patterns at both the ODA Component and node levels.
   • By analyzing historical data and real-time metrics, it forecasts resource demands, enabling proactive planning.

2. ODA Component-Cognitive Recommendations:

   • Based on predicted workload, the Operator dynamically adjusts the number and size of ODA Component / ODA Component groups.
   • Ensures ODA Component receives appropriate resources, preventing over-provisioning or underutilization. (working with Component Lifecycle Manager?)

3. Goal-Driven Resource Allocation:

   • The AI4Canvas Operator collaborates with the ClosedLoop Manager Operator to align resource allocation with ClosedLoop goals.
   • Optimizes compute resource across different ODA Component types within the canvas.

4. Continuous Optimization:

   • The Operator continuously generates resource recommendations, adapting ODA Component resource as new metrics data becomes available.
   • It learns from historical performance, refining predictions for more accurate resource planning.

Prerequisite:

• AI Model manager
• Pattern Prediction Component
• Systems Monitoring and alerting Component (e.g. Prometheus)
• Stream-processing and event store Component (e.g. kafka)
• ClosedLoop manager Component (or similar within an Orchestrator)

(Draft)