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Improving accuracy of host load predictions on computational grids by artificial neural networks

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3 Author(s)
Truong Vinh Truong Duy ; Grad. Sch. of Inf. Sci., Japan Adv. Inst. of Sci. & Technol., Ishikawa, Japan ; Sato, Y. ; Inoguchi, Y.

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.

Published in:

Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on

Date of Conference:

23-29 May 2009

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