Learning Process Energy Profiles from Node-Level Power Data
For data center operators and researchers, this work addresses the need for process-level energy insights to improve energy efficiency, but it is an incremental improvement over existing methods.
The paper tackles the problem of estimating per-process energy consumption in data centers, where existing hardware-based methods like Intel RAPL are limited. The proposed approach uses eBPF and perf to collect fine-grained process-level resource metrics and learns a regression model to predict per-process energy from node-level power data, enabling more fine-grained predictions.
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.