A framework of stochastic power management using hidden Markov model

Ying Tan, Qinru Qiu

Research output: Chapter in Book/Entry/PoemConference contribution

26 Scopus citations


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
Number of pages6
StatePublished - 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


OtherDesign, Automation and Test in Europe, DATE 2008

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'A framework of stochastic power management using hidden Markov model'. Together they form a unique fingerprint.

Cite this