A framework of stochastic power management using hidden Markov model

Ying Tan, Qinru Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning procedure that finds the intrinsic pattern of the incoming tasks based on the observed workload attributes. Markov Decision Process (MDP) based model has been widely adopted for stochastic power management because it delivers provable optimal policy. Given a sequence of observed workload attributes, the hidden Markov model (HMM) of the workload is trained. If the observed workload attributes and states in the workload model do not have one-to-one correspondence, the MDP becomes a Partially Observable Markov Decision Process (POMDP). This paper presents a framework of modeling and optimization for stochastic power management using HMM and POMDP. The proposed technique discovers the HMM of the workload by maximizing the likelihood of the observed attribute sequence. The POMDP optimization is formulated and solved as a quadraticly constrained linear programming (QCLP). Compared with traditional optimization technique, which is based on value iteration, the QCLP based optimization provides superior policy by enabling stochastic control.

Original languageEnglish (US)
Title of host publicationDesign, Automation and Test in Europe, DATE 2008
Pages92-97
Number of pages6
DOIs
StatePublished - Aug 25 2008
Externally publishedYes
EventDesign, Automation and Test in Europe, DATE 2008 - Munich, Germany
Duration: Mar 10 2008Mar 14 2008

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
ISSN (Print)1530-1591

Other

OtherDesign, Automation and Test in Europe, DATE 2008
CountryGermany
CityMunich
Period3/10/083/14/08

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Tan, Y., & Qiu, Q. (2008). A framework of stochastic power management using hidden Markov model. In Design, Automation and Test in Europe, DATE 2008 (pp. 92-97). [4484668] (Proceedings -Design, Automation and Test in Europe, DATE). https://doi.org/10.1109/DATE.2008.4484668