A task scheduling method after clustering for data intensive jobs in heterogeneous distributed systems

Kazuo Hajikano, Hidehiro Kanemitsu, Moo Wan Kim, Hee Dong Kim

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)9-20
Number of pages12
JournalJournal of Computing Science and Engineering
Volume10
Issue number1
DOIs
Publication statusPublished - 2016 Mar 1

Keywords

  • Data intensive
  • Eterogeneous
  • Task clustering
  • Task scheduling

ASJC Scopus subject areas

  • Computer Science Applications
  • Engineering(all)

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