期刊名称：International Journal of Future Generation Communication and Networking
印刷版ISSN：2233-7857
出版年度：2015
卷号：8
期号：2
页码：391-398
DOI：10.14257/ijfgcn.2015.8.2.32
出版社：SERSC
摘要：The job scheduling for Map Reduce clusters has received significant attention in recent years, because it plays an important role on Map Reduce clusters. Traditional job scheduling performs poorly in assigning a task to appropriate nodes, and can not predict the resource utilization of the unexecuted tasks. To address the problems, an efficient job scheduling for Map Reduce clusters is proposed in this paper. The job scheduling introduces dynamic priority scheduling and real-time prediction model. Dynamic priority scheduling introduces the minimum cost data locality algorithm with a weight to deal with different size jobs, and real-time prediction model can predict the resource utilization of unexecuted tasks by calculating the running tasks. The resource utilization contains CPU, memory, and network. Experimental results prove that the proposed job scheduling is able to perform well in Map Reduce clusters.
关键词：job scheduling; minimum cost data locality algorithm; and dynamic priority ; scheduling
