Abstract
In this paper, we present a novel application of connectionist neural modeling to map web page requests to web server cache to maximize hit ratio and at the same time balance the conflicting request of distributing the web requests equally among web caches. In particular, we describe and present a new learning algorithm for a fast Web page allocation on a server using self-organizing properties of neural network. We present a prefetching scheme in which we apply our clustering technique to group users and then prefetch their requests according to the prototype vector of each group. Our prefetching scheme has prediction accuracy as high as 98.18%. A detailed experimental analysis is presented in this paper.
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© 2005 Springer-Verlag Berlin Heidelberg
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You-wei, Y., La-mei, Y., Qing-ping, G. (2005). An Efficient Web Page Allocation on a Server Using Adaptive Neural Networks. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds) Web Technologies Research and Development - APWeb 2005. APWeb 2005. Lecture Notes in Computer Science, vol 3399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31849-1_72
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DOI: https://doi.org/10.1007/978-3-540-31849-1_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25207-8
Online ISBN: 978-3-540-31849-1
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