Neural Network Hot Spot Prediction Algorithm for Shared Web Caching System

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.

[1]  M. K. Soni,et al.  Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods , 2002 .

[2]  Keith W. Ross,et al.  Hash routing for collections of shared Web caches , 1997, IEEE Netw..

[3]  Philip S. Yu,et al.  Analysis of Task Assignment Policies in Scalable Distributed Web-Server Systems , 1998, IEEE Trans. Parallel Distributed Syst..

[4]  David R. Karger,et al.  Web Caching with Consistent Hashing , 1999, Comput. Networks.

[5]  Cheng-Shong Wu,et al.  Efficiency analyses on routing cache replacement algorithms , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[6]  Philip S. Yu,et al.  Replication for Load Balancing and Hot-Spot Relief on Proxy Web Caches with Hash Routing , 2003, Distributed and Parallel Databases.

[7]  Li Fan,et al.  Web caching and Zipf-like distributions: evidence and implications , 1999, IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320).

[8]  Martin F. Arlitt,et al.  Web server workload characterization: the search for invariants , 1996, SIGMETRICS '96.

[9]  Paul Barford,et al.  Generating representative Web workloads for network and server performance evaluation , 1998, SIGMETRICS '98/PERFORMANCE '98.

[10]  Ronald L. Rivest,et al.  The MD5 Message-Digest Algorithm , 1992, RFC.

[11]  Yantai Shu,et al.  Intelligent proxy techniques in plasma physics laboratories , 1999, 1999 IEEE Conference on Real-Time Computer Applications in Nuclear Particle and Plasma Physics. 11th IEEE NPSS Real Time Conference. Conference Record (Cat. No.99EX295).