Web server workload characterization: the search for invariants
Proceedings of the 1996 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Analysis of Task Assignment Policies in Scalable Distributed Web-Server Systems
IEEE Transactions on Parallel and Distributed Systems
Web caching with consistent hashing
WWW '99 Proceedings of the eighth international conference on World Wide Web
Replication for Load Balancing and Hot-Spot Relief on Proxy Web Caches with Hash Routing
Distributed and Parallel Databases
Hash routing for collections of shared Web caches
IEEE Network: The Magazine of Global Internetworking
File access prediction using neural networks
IEEE Transactions on Neural Networks
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There are innumerable objects found on the Web. Both the popular objects that are requested frequently by the users and unpopular objects that are rarely requested exist. Hot spots are produced whenever huge numbers of objects are requested by the users. Often this situation produces excessive load on the cache server and original server, resulting in the system becoming a swamped state. Many problems arise, such as server refusals or slow operations. In this paper, a neural network prediction algorithm is suggested in order to solve the problems caused by the hot spot. The hot spot would be requested in the near future is prefetched to the proxy servers after the prediction of the hot spot; then the fast response for the users' requests and a higher efficiency for the proxy server can be achieved. The hot spots are obtained by analyzing the access logs file. A simulator is developed to validate the performance of the suggested algorithm, through which the hit rate improvement and the request among the shared proxy servers are load-balanced.