Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Image recovery and segmentation using competitive learning in a computational network
Image recovery and segmentation using competitive learning in a computational network
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Dynamic Load Balancing on Web-Server Systems
IEEE Internet Computing
Improving web server performance by caching dynamic data
USITS'97 Proceedings of the USENIX Symposium on Internet Technologies and Systems on USENIX Symposium on Internet Technologies and Systems
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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.