Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jpdc.2007.01.008
Title: A multi-dimensional scheduling scheme in a Grid computing environment
Authors: Khoo, B.T.B
Veeravalli, B. 
Hung, T.
Simon See, C.W.
Keywords: Grid computing
Load distribution
Multiple resources
Parallel processing time
Scheduling
Issue Date: Jun-2007
Source: Khoo, B.T.B, Veeravalli, B., Hung, T., Simon See, C.W. (2007-06). A multi-dimensional scheduling scheme in a Grid computing environment. Journal of Parallel and Distributed Computing 67 (6) : 659-673. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jpdc.2007.01.008
Abstract: In this paper, we propose a novel distributed resource-scheduling algorithm capable of handling multiple resource requirements for jobs that arrive in a Grid computing environment. In our proposed algorithm, referred to as multiple resource scheduling (MRS) algorithm, we take into account both the site capabilities and the resource requirements of jobs. The main objective of the algorithm is to obtain a minimal execution schedule through efficient management of available Grid resources. We first propose a model in which the job and site resource characteristics can be captured together and used in the scheduling algorithm. To do so, we introduce the concept of a n-dimensional virtual map and resource potential. Based on the proposed model, we conduct rigorous simulation experiments with real-life workload traces reported in the literature to quantify the performance. We compare our strategy with most of the commonly used algorithms in place on performance metrics such as job wait times, queue completion times, and average resource utilization. Our combined consideration of job and resource characteristics is shown to render high-performance with respect to above-mentioned metrics in the environment. Our study also reveals the fact that MRS scheme has a capability to adapt to both serial and parallel job requirements, especially when job fragmentation occurs. Our experimental results clearly show that MRS outperforms other strategies and we highlight the impact and importance of our strategy. © 2007 Elsevier Inc. All rights reserved.
Source Title: Journal of Parallel and Distributed Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/54449
ISSN: 07437315
DOI: 10.1016/j.jpdc.2007.01.008
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