JCSE, vol. 10, no. 1, pp.9-20, 2016

DOI: http://dx.doi.org/10.5626/JCSE.2016.10.1.9

A Task Scheduling Method after Clustering for Data Intensive Jobs in Heterogeneous Distributed Systems

Kazuo Hajikano, Hidehiro Kanemits, Moo Wan Kim and Hee-Dong Kim
*Department of Information Technology and Electronics, Daiichi Institute of Technology, Kagoshima, Japan Global Education Center, Waseda University, Tokyo, Japan Department of Informatics, Tokyo University of Information Sciences, Chiba, Japan Department of Information & Communications Engineering, Hankuk University of Foreign Studies, Yongin, Korea

Abstract: Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, one of the challenging problems is the process in task scheduling after task allocation by task clustering. We propose a task scheduling method after task clustering, leveraging worst schedule length (WSL) as an upper bound of the schedule length. In our proposed method, a task in a WSL sequence is scheduled preferentially to make the WSL smaller. Experimental results by simulation show that the response time is improved in several task clustering heuristics. In particular, our proposed scheduling method with the task clustering outperforms conventional list-based task scheduling methods.

Keyword: Task clustering; Task scheduling; Heterogeneous; Data intensive

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