~/talk

Greening the AI Cloud: Validating Power Models for Kubernetes Containers

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Machine learning drives up to 40% of data center energy and Kepler framework measures per-process, container, pod and job power using eBPF collectors for low overhead telemetry, motherboard sensors on bare metal, and pre-trained VM power models built with regression when sensors are unavailable. Kepler runs as a CNCF Sandbox project, exposes Prometheus/Grafana metrics, partitions CPU/GPU/DRAM power (GPU driver readouts when available), and validates model estimates against node sensors showing closely matching results in MLPerf inference and training runs.

AI-written summary · about

  1. 0:00Energy measurement in cloud apps
  2. 1:02Motivation for AI workload energy
  3. 5:45Kepler project overview
  4. 7:13Kepler architecture and power model
  5. 10:11Bare metal vs VM deployments
  6. 13:21Power model limitations
  7. 14:25GPU energy measurement
  8. 16:11MLPerf benchmark tool
  9. 18:19Kepler on Kubernetes
  10. 21:27MLPerf inference with Kepler
  11. 22:29Kepler Grafana dashboard
  12. 23:33Validating Kepler power measurements
  13. 24:56Summary and references
  14. 25:18Q&A on cloud energy
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