An adaptive job allocation strategy for heterogeneous multi-cluster systems

Authors:
Chao-Tung Yang;Keng-Yi Chou;Kuan-Chou Lai
Affiliations:
High-Performance Computing Laboratory, Department of Computer Science, Tunghai University, Taichung, Taiwan (ROC);High-Performance Computing Laboratory, Department of Computer Science, Tunghai University, Taichung, Taiwan (ROC);Department of Computer and Information Science, National Taichung University, Taichung, Taiwan (ROC)
Venue:
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Year:
2010

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Abstract

In this paper, we propose a new job allocation system for multi-clusters environments, named the Adaptive Job Allocation Strategy (AJAS), in which a scheduler uses a self-scheduling scheme to dispatch jobs to appropriate distributed resources Our strategy focuses on increasing resource utility by dispatching jobs to computing nodes with similar performance capacities to equalize job execution times among all nodes The experimental results show that AJAS could indeed to improve the system performance.