An adaptive data replication algorithm
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IEEE Transactions on Parallel and Distributed Systems
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Recently, data replication algorithms have been widely employed in data grids to replicate frequently accessed data to appropriate sites. The purposes are shortening file transmission distance and delivering files from nearby sites to local sites so as to improve data access performance and reduce bandwidth consumption. Some of the algorithms were designed based on unlimited storage. However, they might not be practical in real-world data grids since currently no system has infinite storage. Others were implemented on limited storage environments, but none of them considers data access patterns which reflect the changes of users' interests, and these are important parameters affecting file retrieval efficiency and bandwidth consumption. In this paper, we propose an adaptive data replication algorithm, called the Popular File Replicate First algorithm (PFRF for short), which is developed on a star-topology data grid with limited storage space based on aggregated information on previous file accesses. The PFRF periodically calculates file access popularity to track the variation of users' access behaviors, and then replicates popular files to appropriate sites to adapt to the variation. We employ several types of file access behaviors, including Zipf-like, geometric, and uniform distributions, to evaluate PFRF. The simulation results show that PFRF can effectively improve average job turnaround time, bandwidth consumption for data delivery, and data availability as compared with those of the tested algorithms.