Title
Hybrid Genetic Algorithm for Cloud Computing Applications
Document Type
Conference Presentation
Department
Computer Science
Conference Title
2011 IEEE Asia-Pacific Services Computing Conference
Location
Jeju Island, South Korea
Conference Dates
December 12-15, 2011
Date of Presentation
12-12-2011
Abstract
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.
DOI
10.1109/APSCC.2011.66
Recommended Citation
Zhu, K.,
Song, H.,
Liu, L.,
Gao, J.,
&
Cheng, G.
(2011).
Hybrid Genetic Algorithm for Cloud Computing Applications.
Paper presented at 2011 IEEE Asia-Pacific Services Computing Conference in Jeju Island, South Korea.
https://scholarlycommons.pacific.edu/soecs-facpres/66
