System-level power management using online learning

  • Authors:
  • Gaurav Dhiman;Tajana Šimunic Rosing

  • Affiliations:
  • Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA;Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA

  • Venue:
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
  • Year:
  • 2009

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Abstract

In this paper, we propose a novel online-learning algorithm for system-level power management. We formulate both dynamic power management (DPM) and dynamic voltage-frequency scaling problems as one of workload characterization and selection and solve them using our algorithm. The selection is done among a set of experts, which refers to a set of DPM policies and voltage-frequency settings, leveraging the fact that different experts outperform each other under different workloads and device leakage characteristics. The online-learning algorithm adapts to changes in the characteristics and guarantees fast convergence to the best-performing expert. In our evaluation, we perform experiments on a hard disk drive (HDD) and Intel PXA27x core (CPU) with real-life workloads. Our results show that our algorithm adapts really well and achieves an overall performance comparable to the best-performing expert at any point in time, with energy savings as high as 61% and 49% for HDD and CPU, respectively. Moreover, it is extremely lightweight and has negligible overhead.