Dynamic power management using machine learning

Authors:
Gaurav Dhiman;Tajana Simunic Rosing
Affiliations:
University of California, San Diego;University of California, San Diego
Venue:
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Year:
2006

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Abstract

Dynamic power management (DPM) work proposed to date places inactive components into low power states using a single DPM policy. In contrast, we instead dynamically select among a set of DPM policies with a machine learning algorithm. We leverage the fact that different policies outperform each other under different workloads and devices. Our algorithm adapts to changes in workloads and guarantees quick convergence to the best performing policy for each workload. We performed experiments with a policy set representing state of the art DPM policies on a hard disk drive and a WLAN card. Our results show that our algorithm adapts really well with changing device and workload characteristics and achieves an overall performance comparable to the best performing policy at any point of time.