Autonomous SPY©: Intelligent Web proxy caching detection using neurocomputing and Particle Swarm Optimization

The demand for Internet content rose dramatically. Servers became more and more powerful and the bandwidth of end user connections and backbones grew constantly. Nevertheless users often experience poor performance when they access Web sites or download files. Reasons for such problems are often performance problems which occur directly on the servers (e.g. poor performance of server-side applications or during flash crowds), problems concerning the network infrastructure (e.g. long geographical distances, network overloads, etc.) and a surprising fact is that many people tend to access the same piece of information repeatedly. Web caching has been recognized as the effective schemes to alleviate the service bottleneck, minimize the user access latency and reduce the network traffic. Consequently, in this paper we discuss an alternative way to tackle these problems with an implementation of autonomous SPY tool. This tool is capable to self directed either to cache or not to cache the objects in a document based on the behavior of users' activities (number of object hits, script size of objects, and time to receive objects) in Internet based electronic services (e-services) for enhancing Web access.