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ROADMAP.md

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Roadmap

1. Core Functionality

1.1 Power Capping Configuration

  • Implement the ability to create and manage power capping configurations using Custom Resource Definitions (CRDs).
  • Define the schema for the power capping configuration, including the power cap limit and scale object references.
  • Validate the power capping configuration against the defined schema.

1.2 Monitoring Power Usage

  • Integrate with Prometheus to retrieve power consumption metrics.
  • Implement a periodic monitoring mechanism to check the power usage against the power cap limit.
  • Adjust the scaling behavior of the associated scale objects based on the power usage levels.

1.3 Scaling Adjustments

  • Retrieve the current number of replicas from the associated Kubernetes deployments.
  • Implement logic to set the maximum replica count based on the power usage levels and power cap limit.
  • Update the scale objects with the adjusted maximum replica count to enforce power capping.

2. Integration

2.1 Kubernetes Deployment Integration

  • Enhance the operator to handle Kubernetes deployments as the scale target.
  • Retrieve the current replica count from the deployment status.
  • Adjust the maximum replica count of the deployments based on the power capping configuration.

2.2 KServe Integration

  • Integrate the power capping operator with KServe.
  • Ensure power capping is applied to KServe deployments and associated scale objects.
  • Provide seamless integration with KServe components and workflows.

2.3 vLLM Integration

  • Extend the power capping operator to support vLLM deployments.
  • Apply power capping to vLLM deployments and associated scale objects in a similar manner to KServe.
  • Ensure compatibility and seamless integration with vLLM components.

2.4 Real-Time Carbon Intensity Integration

  • Identify and integrate real-time carbon intensity data into the power capping operator.
  • Fetch carbon intensity data from external APIs or data sources.
  • Calculate carbon emission based on power usage and carbon intensity.
  • Dynamically adjust the power cap based on carbon intensity to achieve target carbon capping.

3. Enhancements

3.1 Power Efficiency Aware LLM Inference Routing

  • Implement power efficiency aware routing for LLM inference requests.
  • Utilize a Layer 7 router to route requests to LLM inference services with the highest token/watts ratio.
  • Integrate with the power capping operator to optimize power efficiency and maximize token throughput.

3.2 GPU Frequency Tuning for Optimal Power Efficiency

  • Implement GPU frequency tuning capabilities to optimize power efficiency.
  • Develop a Kubernetes job to periodically tune GPU frequencies based on power consumption metrics.
  • Integrate with the power capping operator to adjust GPU frequencies and maximize token throughput within the power cap limit.

3.3 Power Usage Smoothing

  • Enhance the power capping operator to support power usage smoothing techniques.
  • Implement workload-aware service placement to distribute workloads evenly across power nodes.
  • Utilize dynamic power profile reshaping to optimize power usage and maximize throughput.
  • Integrate with KEDA for power-aware scaling of LLM inference services.

4. Monitoring and Observability

4.1 Prometheus Integration

  • Integrate the power capping operator with Prometheus for monitoring power consumption metrics.
  • Expose relevant metrics from the operator for Prometheus scraping.
  • Utilize Prometheus queries to retrieve power usage data for decision-making.

4.2 Grafana Dashboard

  • Develop a Grafana dashboard to visualize power consumption metrics and operator performance.
  • Display real-time power usage, carbon emission, and scaling behavior of LLM inference services.
  • Provide insights into power efficiency and carbon capping effectiveness.

5. Testing and Validation

5.1 Unit Testing

  • Implement comprehensive unit tests for the power capping operator codebase.
  • Cover core functionality, integration points, and enhancement features.
  • Ensure high code coverage and maintain test quality.

5.2 Simulated Integration Testing

  • Develop integration tests to validate the operator's behavior in a simulated cluster environment.
  • Test integration with Kubernetes deployments, KServe, vLLM, and carbon intensity data sources in a simulator.
  • Verify the operator's ability to enforce power capping and achieve target carbon capping in a simulator.

5.3 Integration Testing

  • Develop integration tests to validate the operator's behavior in a real cluster environment.
  • Test integration with Kubernetes deployments, KServe, vLLM, and carbon intensity data sources.
  • Verify the operator's ability to enforce power capping and achieve target carbon capping.

5.4 Performance Testing

  • Conduct performance tests to evaluate the operator's scalability and efficiency.
  • Measure the impact of power capping on LLM inference throughput and latency.
  • Optimize the operator's performance based on the test results.

6. Documentation and User Guide

6.1 Operator Installation Guide

  • Provide detailed instructions for installing the power capping operator in a Kubernetes cluster.
  • Include prerequisites, deployment steps, and configuration options.
  • Cover integration with required components and services.

6.2 User Guide and Examples

  • Develop a comprehensive user guide for the power capping operator.
  • Provide examples and tutorials on how to configure and use the operator effectively.
  • Include best practices and troubleshooting tips for common scenarios.

6.3 API Reference

  • Document the API reference for the power capping configuration CRD.
  • Describe the available fields, their meanings, and usage guidelines.
  • Provide examples of API usage and expected responses.

7. Community and Collaboration

7.1 Open Source Contribution

  • Publish the power capping operator as an open-source project, submit to CNCF Sandbox.

7.2 Ecosystem Integration

  • Collaborate with the Kubernetes, KServe, vLLM, and other communities to ensure seamless integration.
  • Provide support for cross-project features and compatibility.
  • Contribute back to the ecosystem by sharing knowledge and expertise.

8. Simulation and Validation

8.1 Power Capping Simulation with Real-World Traces

  • Develop a simulation framework to evaluate the power capping capabilities using real-world power consumption traces.
  • Collect power consumption data from production environments or representative workloads.
  • Simulate the behavior of the power capping operator using the collected traces.
  • Analyze the effectiveness of the power capping operator in managing power usage within the defined limits.
  • Evaluate the impact of different timer settings and configurations on the success rate of power capping.