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Hybrid Genetic Algorithm for Cloud Computing Applications

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5 Author(s)
Zhu, Kai ; Sch. of Eng. & Comput. Sci., Univ. of the Pacific, Stockton, CA, USA ; Huaguang Song ; Lijing Liu ; Gao, Jinzhu
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In the cloud computing system, the schedule of computing resources is a critical portion of cloud computing study. An effective load balancing strategy is able to markedly improve the task throughput of cloud computing. Virtual machines are selected as a fundamental processing unit of cloud computing. The resources in cloud computing will increase sharply and vary dynamically due to the utilization of virtualization technology. Therefore, implementation of load balancing in cloud computing has become complicated and it is difficult to achieve. Multi-agent genetic algorithm (MAGA) is a hybrid algorithm of GA, whose performance is far superior to that of the traditional GA. This paper demonstrates the advantage of MAGA over traditional GA, and then exploits multi-agent genetic algorithms to solve the load balancing problem in cloud computing, by designing a load balancing model on the basis of virtualization resource management. Finally, by comparing MAGA with Minimum strategy, the experiment results prove that MAGA is able to achieve better performance of load balancing.

Published in:

Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific

Date of Conference:

12-15 Dec. 2011