Learning Process Energy Profiles from Node-Level Power Data

TL;DR

This paper introduces a method to estimate per-process energy consumption in data centers by combining fine-grained resource metrics with node-level energy data using regression models.

Contribution

It presents a novel approach leveraging eBPF and perf to model process-level energy profiles, improving energy efficiency insights beyond existing hardware limitations.

Findings

Accurately models per-process energy using resource metrics and node-level measurements.

Enables fine-grained energy predictions for individual processes.

Improves understanding of energy consumption in data centers.

Abstract

The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increase in data center energy consumption. To improve energy efficiency, gaining process-level insights into energy consumption is essential. While node-level energy consumption data can be directly measured with hardware such as power meters, existing mechanisms for estimating per-process energy usage, such as Intel RAPL, are limited to specific hardware and provide only coarse-grained, domain-level measurements. Our proposed approach models per-process energy profiles by leveraging fine-grained process-level resource metrics collected via eBPF and perf, which are synchronized with node-level energy measurements obtained from an attached power distribution unit. By statistically learning the relationship between process-level resource usage and node-level energy consumption through a regression-based model, our approach enables more fine-grained per-process energy predictions.