Energy Efficient Speed Scaling and Task Scheduling for Distributed Computing Systems

HUANG Jiwei; LIN Chuang; CHENG Bo

doi:10.1049/cje.2015.07.005

Volume 24 Issue 3

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HUANG Jiwei, LIN Chuang, CHENG Bo, “Energy Efficient Speed Scaling and Task Scheduling for Distributed Computing Systems,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 468-473, 2015, doi: 10.1049/cje.2015.07.005

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HUANG Jiwei, LIN Chuang, CHENG Bo, “Energy Efficient Speed Scaling and Task Scheduling for Distributed Computing Systems,” Chinese Journal of Electronics, vol. 24, no. 3, pp. 468-473, 2015, doi: 10.1049/cje.2015.07.005

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Energy Efficient Speed Scaling and Task Scheduling for Distributed Computing Systems

doi: 10.1049/cje.2015.07.005

1.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2.
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Funds: This work is supported by the National Grand Fundamental Research 973 Program of China (No.2012CB315802), National High-tech R&D Program of China (863 Program) (No.2013AA102301), National Natural Science Foundation of China (No.61132001), Program for New Century Excellent Talents in University (No.NCET-11-0592), and the Fundamental Research Funds for the Central Universities (No.2015RC22).

Received Date: 2014-08-31
Rev Recd Date: 2015-01-05
Publish Date: 2015-07-10

Abstract

Abstract

With the rapid development of the performance of computer systems, energy consumption has been increasing dramatically as well. Energy efficiency has been paid much attention, especially in large-scale distributed computing systems. We propose an effective approach for energy reduction, by dynamic speed scaling and task scheduling simultaneously. Markov models for distributed computing systems are proposed, and detailed analyses of the models are provided. Markov decision processes (MDP) are applied for problem formulation, and MDP algorithms for obtaining the optimal solutions are introduced. The efficacy of our approach is further validated by simulation experiments.
- Energy efficiency,
- Speed scaling,
- Task scheduling,
- Markov decision process,
- Distributed computing system

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References(19)

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