Rough set clustering approach to replica selection in data grids (RSCDG)

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dc.contributor.author Almuttairi, Rafah M.
dc.contributor.author Wankar, Rajeev
dc.contributor.author Negi, Atul
dc.contributor.author Chillarige, Raghavendra Rao
dc.date.accessioned 2022-03-27T00:16:59Z
dc.date.available 2022-03-27T00:16:59Z
dc.date.issued 2010-12-01
dc.description.abstract In data grids, the fast and proper replica selection decision leads to better resource utilization due to reduction in latencies to access the best replicas and speed up the execution of the data grid jobs. In this paper, we propose a new strategy that improves replica selection in data grids with the help of the reduct concept of the Rough Set Theory (RST). Using Quickreduct algorithm the unsupervised clustering is changed into supervised reducts. Then, Rule algorithm is used for obtaining optimum rules to derive usage patterns from the data grid information system. The experiments are carried out using Rough Set Exploration System (RSES) tool. © 2010 IEEE.
dc.identifier.citation Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
dc.identifier.uri 10.1109/ISDA.2010.5687024
dc.identifier.uri http://ieeexplore.ieee.org/document/5687024/
dc.identifier.uri https://dspace.uohyd.ac.in/handle/1/3224
dc.subject Data grid
dc.subject K-means
dc.subject Quickreduct
dc.subject Replica selection strategies
dc.subject Rough Set Theory (RST)
dc.subject Rule algorithm
dc.title Rough set clustering approach to replica selection in data grids (RSCDG)
dc.type Conference Proceeding. Conference Paper
dspace.entity.type
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