Efficient Routing Configuration of the Web Page Requests Based on Feed-forward Neural Networks

摘要

Convex hull of any subset of vertices of an n-dimensional hypercube contains no other vertex of the hypercube. This result permits the application of some theorems of n-dimensional geometry to digital feed-forward neural networks. 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. The paper presents results obtained by using sl feed forward neural network architectures for transcription, namely multilayer perceptrons, RBF networks, support vector machines and time-delay networks.