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Title: Binary-Tree Based Estimation of File Requests for Efficient Data Replication
Authors: Souravlas, Stavros
Sifaleras, Angelo
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: Data Replication
Binary Trees
Data Grid
File Popularity
Issue Date: 2017
Publisher: IEEE
Source: IEEE Transactions on Parallel and Distributed Systems
Volume: 28
Issue: 7
First Page: 1839
Last Page: 1852
Abstract: Recently, data replication has received considerable attention in the field of grid computing. The main goal of data replication algorithms is to optimize data access performance by replicating the most popular files. When a file does not exist in the node where it was requested, it necessarily has to be transferred from another node, causing delays in the completion the file requests. The general idea behind data replication is to keep track of the most popular files requested in the grid and create copies of them in selected nodes. In this way, more file requests can be completed over a period of time and average job execution time is reduced. In this paper, we introduce an algorithm that estimates the potential of the files located in each node of the grid, using a binary tree structure. Also, the file scope and the file type are taken into account. By potential of a file, we mean its increasing or decreasing demand over a period of time. The file scope generally refers to the extent of the group of users which are interested or potentially interested in a file. The file types are divided into read and write intensive. Our scheme mainly promotes the high-potential files for replication, based on the temporal locality principle. The simulation results indicate that the proposed scheme can offer better data access performance in terms of the hit ratio and the average job execution time, compared to other state-of-the-art strategies.
ISSN: 1045-9219
Other Identifiers: 10.1109/TPDS.2017.2650228
Appears in Collections:Department of Applied Informatics

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