A taxonomy of application scheduling tools for high performance cluster computing

Authors:
Jiannong Cao;Alvin T. Chan;Yudong Sun;Sajal K. Das;Minyi Guo
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;School of Computing Science, University of Newcastle upon Type, Newcastle upon Type, UK NE1 7RU;Department of Computer Software, University of Aizu, Aizu-Wakamatsu City, Fukushima, Japan 965-8580;Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, USA 76019-0015
Venue:
Cluster Computing
Year:
2006

Citing 0
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Abstract

Application scheduling plays an important role in high-performance cluster computing. Application scheduling can be classified as job scheduling and task scheduling. This paper presents a survey on the software tools for the graph-based scheduling on cluster systems with the focus on task scheduling. The tasks of a parallel or distributed application can be properly scheduled onto multi-processors in order to optimize the performance of the program (e.g., execution time or resource utilization). In general, scheduling algorithms are designed based on the notion of task graph that represents the relationship of parallel tasks. The scheduling algorithms map the nodes of a graph to the processors in order to minimize overall execution time. Although many scheduling algorithms have been proposed in the literature, surprisingly not many practical tools can be found in practical use. After discussing the fundamental scheduling techniques, we propose a framework and taxonomy for the scheduling tools on clusters. Using this framework, the features of existing scheduling tools are analyzed and compared. We also discuss the important issues in improving the usability of the scheduling tools.