An Improved Adaptive Genetic Algorithm in Cloud Computing

被引：5

作者：

Hu Baofang ^{[1
]}

Sun Xiuli ^{[2
]}

Li Ying ^{[1
]}

Sun Hongfeng ^{[1
]}

机构：

[1] Shandong Womens Univ, Dept Informat Technol, Jinan, Shandong, Peoples R China

[2] Shandong Womens Univ, Jinan, Shandong, Peoples R China

来源：

2012 13TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING, APPLICATIONS, AND TECHNOLOGIES (PDCAT 2012) | 2012年

关键词：

cloud computing; genetic algorithm; adaptive genetic algorithm; convergence rate;

D O I：

10.1109/PDCAT.2012.47

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：294 / 297

页数：4