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Presents the simulation of 27 proxy cache replacement strategies, reviewing these by several important performance measures
Introduces the novel Neural Network Proxy Cache Replacement (NNPCR) approach, which utilizes neural networks for replacement decisions
Examines the implementation of NNPCR in a real environment
This work presents a study of cache replacement strategies designed for static web content. Proxy servers can improve performance by caching static web content such as cascading style sheets, java script source files, and large files such as images. This topic is particularly important in wireless ad hoc networks, in which mobile devices act as proxy servers for a group of other mobile devices. Opening chapters present an introduction to web requests and the characteristics of web objects, web proxy servers and Squid, and artificial neural networks. This is followed by a comprehensive review of cache replacement strategies simulated against different performance metrics. The work then describes a novel approach to web proxy cache replacement that uses neural networks for decision making, evaluates its performance and decision structures, and examines its implementation in a real environment, namely, in the Squid proxy server.
Content Level »Research
Keywords »Cache Replacement - NNPCR - Neural Networks - Neural Networks Training - Performance Evaluation - Proxy Cache - Proxy Server - Simulation - Squid - Web