Using swarm intelligence to optimize the energy consumption for distributed systems

Bergmann, Neil, Chung, Yuk Ying, Yang, Xiangrui, Chen, Zhe, Yeh, Wei Chang, He, Xiangjian and Jurdak, Raja (2013) Using swarm intelligence to optimize the energy consumption for distributed systems. Modern Applied Science, 7 6: 59-66. doi:10.5539/mas.v7n6p59

Author Bergmann, Neil
Chung, Yuk Ying
Yang, Xiangrui
Chen, Zhe
Yeh, Wei Chang
He, Xiangjian
Jurdak, Raja
Title Using swarm intelligence to optimize the energy consumption for distributed systems
Journal name Modern Applied Science   Check publisher's open access policy
ISSN 1913-1844
Publication date 2013-05-21
Year available 2013
Sub-type Article (original research)
DOI 10.5539/mas.v7n6p59
Open Access Status DOI
Volume 7
Issue 6
Start page 59
End page 66
Total pages 8
Place of publication Toronto, ON, Canada
Publisher Canadian Center of Science and Education
Collection year 2014
Language eng
Abstract Large, distributed, network-based computing systems (also known as Cloud Computing) have recently gained significant interest. We expect significantly more applications or web services will be relying on network-based servers, therefore reducing the energy consumption of these systems would be beneficial for companies to save their budgets on running their machines as well as cooling down their infrastructures. Dynamic Voltage Scaling can save significant energy for these systems, but it faces the challenge of efficient and balanced parallelization of tasks in order to maximize energy savings while maintaining desired performance levels. This paper proposes our Simplified Swarm Optimization (SSO) method to reduce the energy consumption for distributed systems with Dynamic Voltage Scaling. The results of SSO have been compared to the most popular evolutionary Particle Swarm Optimization (PSO) algorithm and have shown to be more efficient and effective, reducing both the execution time for scheduling and makespan.
Keyword Energy optimization
Evolutionary algorithm
Distributed computing
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 21 May 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Created: Wed, 22 May 2013, 11:17:11 EST by Professor Neil Bergmann on behalf of School of Information Technol and Elec Engineering