Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

M. Triki, Y. Wang, A. C. Ammari, M. Pedram

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.

Original languageEnglish (US)
Pages (from-to)10-20
Number of pages11
JournalIntegration, the VLSI Journal
Volume48
Issue number1
DOIs
StatePublished - Jan 1 2015

Keywords

  • Power management
  • Reinforcement learning
  • Semi-Markov decision process
  • Temporal difference learning

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Hierarchical power management of a system with autonomously power-managed components using reinforcement learning'. Together they form a unique fingerprint.

  • Cite this