DocumentCode :
2977173
Title :
An Improved Adaptive Genetic Algorithm in Cloud Computing
Author :
Hu Baofang ; Sun Xiuli ; Li Ying ; Sun Hongfeng
Author_Institution :
Dept. of Inf. Technol., Shandong Women´s Univ., Jinan, China
fYear :
2012
fDate :
14-16 Dec. 2012
Firstpage :
294
Lastpage :
297
Abstract :
Aiming at the task scheduling algorithm of cloud environment, an improved adaptive genetic algorithm (PAGA) based on priority mechanism is proposed. This approach for job scheduling not only ensures to make the least execution time but also guarantees the QoS requirement of customer job. An integrated fitness function based on priority is designed to indicate optimized object. This method has advantages of simplifying the iterative operation and reducing iteration times. The proposed algorithm is being compared with the other scheduling algorithms. The experimental result shows that this algorithm has high convergence rate.
Keywords :
cloud computing; genetic algorithms; iterative methods; scheduling; PAGA; QoS requirement; cloud computing; improved adaptive genetic algorithm; integrated fitness function; iterative operation; job scheduling; priority mechanism; task scheduling algorithm; Biological cells; Cloud computing; Convergence; Educational institutions; Encoding; Genetic algorithms; Processor scheduling; adaptive genetic algorithm; cloud computing; convergence rate; genetic algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-4879-1
Type :
conf
DOI :
10.1109/PDCAT.2012.47
Filename :
6589280
Link To Document :
بازگشت