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

Abstract

Several task clustering heuristics are proposed for allocating tasks in heterogeneous systems to achieve a good response time in data intensive jobs. However, it has been one of challenging problems that how each task should be scheduled after a task allocation by a 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 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 we proposed previously outperforms conventional list-based task scheduling methods.