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General Information
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2012 Vol.2(6): 697-700 ISSN: 2010-3697
DOI: 10.7763/IJMO.2012.V2.213

Job Scheduling for Heterogeneous Computing Environments

P. Devaki and M. L. Valarmathi

Abstract—Scheduling the jobs of computationally intensive applications efficiently is one of the most essential and difficult issues when aiming at high performance in heterogeneous computing environments. A large number of scheduling heuristics have been presented in literature for homogeneous computing systems. The complexity of the problem increases when job scheduling is to be carried out in heterogeneous computing system. In this paper, we present a simple algorithm Heterogeneous Task Scheduling (HTS) for a bounded number of heterogeneous machines. The aim of our algorithm is to minimize the overall completion time of jobs (makespan) submitted. The methodology used is to maintain the dynamic queue (Ready Queue) in which the ready jobs are available. From that queue the job is selected for execution on a machine which is capable of completing that job quickly. The analysis and experiments have shown that this algorithm provides comparable results in some cases and even better results in most of the cases together with low complexity when compared with the existing algorithms Critical-Path On a Processor (CPOP) and Heterogeneous Critical Parent Trees (HCPT).

Index Terms—Scheduling, computationally intensive, heterogeneous system, makespan.

Ms. P. Devaki and M. L. Valarmathi are with Department of CSE, Kumaraguru College of Technology, Coimbatore Tamil Nadu, India (email: devaki_cbe4@yahoo.com; ml_valarmathi@rediffmail.com).


Cite: P. Devaki and M. L. Valarmathi, "Job Scheduling for Heterogeneous Computing Environments," International Journal of Modeling and Optimization vol. 2, no. 6, pp. 697-700, 2012.

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