The research and Improvement of MapReduce cluster scheduling strategy based on ant colony optimization

被引：1
|
作者：
Zhang, Yaping [1 ]
Qin, Jun
Zhai, Zhao [1 ]
机构：
[1] Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210003, Jiangsu, Peoples R China
来源：
APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY | 2014年 / 513-517卷
关键词：
Ant Colony Optimization; MapReduce Clusters; Schedule Strategy;
D O I：
10.4028/www.scientific.net/AMM.513-517.1989
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The distributed computing framework of MapReduce has been widely used in big companies as a powerful tool for processing large scale of data. This paper will introduce the existing algorithms about MapReduce job scheduling and analyze the major two ones. It points out the defects of multi-task scheduling application on processing massive jobs and proposes a kind of multi-task cluster schedule algorithm MSBACO which is based on the ant colony optimization. The results of the experiments have proved its effectiveness and stability in heterogeneous environment.
引用
收藏
页码：1989 / 1993
页数：5
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