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Parallel and Distributed Processing Symposium, International (2009)
Rome, Italy
May 23, 2009 to May 29, 2009
ISBN: 978-1-4244-3751-1
pp: 1-8
Truong Vinh Truong Duy , Graduate School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292 Japan
Yukinori Sato , Center for Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292 Japan
Yasushi Inoguchi , Center for Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292 Japan
ABSTRACT
The capability to predict the host load of a system is significant for computational grids to make efficient use of shared resources. This paper attempts to improve the accuracy of host load predictions by applying a neural network predictor to reach the goal of best performance and load balance. We describe feasibility of the proposed predictor in a dynamic environment, and perform experimental evaluation using collected load traces. The results show that the neural network achieves a consistent performance improvement with surprisingly low overhead. Compared with the best previously proposed method, the typical 20:10:1 network reduces the mean and standard deviation of the prediction errors by approximately 60% and 70%, respectively. The training and testing time is extremely low, as this network needs only a couple of seconds to be trained with more than 100,000 samples in order to make tens of thousands of accurate predictions within just a second.
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CITATION

Yukinori Sato, Y. Inoguchi and Truong Vinh Truong Duy, "Improving accuracy of host load predictions on computational grids by artificial neural networks," 2009 IEEE International Symposium on Parallel & Distributed Processing (IPDPS), Rome, 2009, pp. 1-8.
doi:10.1109/IPDPS.2009.5160878
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