On Construction of a Multi-Grid Resource Selection Strategy on Grids

On Construction of a Multi-Grid Resource Selection Strategy on Grids

Chao-Tung Yang, Wen-Jen Hu, Kuan-Chou Lai
Copyright: © 2014 |Pages: 25
DOI: 10.4018/ijghpc.2014010103
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Grid computing is now in widespread use, integrating geographical computing resources across multiple virtual organizations to achieve high performance computing. A single grid does not often provide a vast resource because virtual organizations have inadequate computing resource restrictions for management on an organizational scale. This paper presents a new grid architecture named Multi-Grid, which integrates multiple computational grids from different virtual organizations. This study builds a resource broker on multiple grid environments, integrating a number of single grids from different virtual organizations without the limit of organizations. The purpose of the multiple-grid resource is to avoid wasting resources. In addition, this study proposes a Multi-Grid Resource Selection Strategy (MRGSS) for the resource broker to better allocate resources before submitting jobs, to avoid network congestion that consequently causes a decrease in performance.
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1. Introduction

Grid technology plays a major role in managing large-scale problems, by integrating distributed resources to provide users with a supercomputer with a capacity for data sharing and computation (Tang & Zhang, 2005; Ian & Carl, 1997; Ian & Carl, 1999). Participating sites may be physically distributed, heterogeneous, and governed by different administrative domains. Numerous related studies and projects have proposed solutions for large-scale scientific problems, such as earthquake simulation, atmosphere and ocean simulation, high energy and nuclear physics, bioinformatics, and medical image processing. A number of other proposed grid projects are Globus, Condor, LEGION, Grid PP, EGEE, P-Grid, DutchGrid, ESnet, and Grid Bus (http://nws.cs.ucsb.edu/ewiki/) tools to monitor resource status and network-related information (Yang, Shih, & Chen, 2006), respectively. Understanding the influence of each parameter is not only crucial for an application to achieve a good performance, but also helps develop effective schedule heuristics and design high-quality grids.

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