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Replica Placement Strategy for Data Grid Environment

Replica Placement Strategy for Data Grid Environment

Mohammed K. Madi, Yuhanis Yusof, Suhaidi Hassan
Copyright: © 2013 |Volume: 5 |Issue: 1 |Pages: 12
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466631564|DOI: 10.4018/jghpc.2013010105
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MLA

Madi, Mohammed K., et al. "Replica Placement Strategy for Data Grid Environment." IJGHPC vol.5, no.1 2013: pp.70-81. http://doi.org/10.4018/jghpc.2013010105

APA

Madi, M. K., Yusof, Y., & Hassan, S. (2013). Replica Placement Strategy for Data Grid Environment. International Journal of Grid and High Performance Computing (IJGHPC), 5(1), 70-81. http://doi.org/10.4018/jghpc.2013010105

Chicago

Madi, Mohammed K., Yuhanis Yusof, and Suhaidi Hassan. "Replica Placement Strategy for Data Grid Environment," International Journal of Grid and High Performance Computing (IJGHPC) 5, no.1: 70-81. http://doi.org/10.4018/jghpc.2013010105

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Abstract

Data Grid is an infrastructure that manages huge amount of data files, and provides intensive computational resources across geographically distributed collaboration. To increase resource availability and to ease resource sharing in such environment, there is a need for replication services. Data replication is one of the methods used to improve the performance of data access in distributed systems by replicating multiple copies of data files in the distributed sites. Replica placement mechanism is the process of identifying where to place copies of replicated data files in a Grid system. Existing work identifies the suitable sites based on number of requests and read cost of the required file. Such approaches consume large bandwidth and increases the computational time. The authors propose a replica placement strategy (RPS) that finds the best locations to store replicas based on four criteria, namely, 1) Read Cost, 2) File Transfer Time, 3) Sites’ Workload, and 4) Replication Sites. OptorSim is used to evaluate the performance of this replica placement strategy. The simulation results show that RPS requires less execution time and consumes less network usage compared to existing approaches of Simple Optimizer and LFU (Least Frequently Used).

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