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18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 13
Predicting the Performance of GridFTP Transfers
Santa Fe, New Mexico
April 26-April 30
ISBN: 0-7695-2132-0
Rashedur M. Rahman, University of Calgary
Ken Barker, University of Calgary
Reda Alhajj, University of Calgary
Replication is a technique in Data Grid environment that helps to reduce access latency and network bandwidth. Replication also increases data availability and thereby enhances the reliability of the system. Selecting the best replica depends on several factors such as past behavior of the transfer, current state of the network as well as the state of disk device. In this paper, we develop a predictive framework with a neural network that uses the data from various sources and predicts transfer bandwidth. We compare our results with regression models and demonstrate that the neural network technique outperforms the regression model based predictors for large file transfers.
Index Terms:
Grid, neural network, replica, regression model, prediction
Citation:
Rashedur M. Rahman, Ken Barker, Reda Alhajj, "Predicting the Performance of GridFTP Transfers," ipdps, vol. 14, pp.238a, 18th International Parallel and Distributed Processing Symposium (IPDPS'04) - Workshop 13, 2004
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