A Self-Learning based Adaptive Clustering in a Wireless Internet Proxy Server Environment
Hu Keun Kwak, Kyusik Chung · Jeongbo gwahaghoe nonmunji. si'seu'tem mich i'lon · 2006
A clustering based wireless internet proxy server with cooperative caching has a problem of minimizing overall performance because some servers become overloaded if client request pattern is Hot-Spot or uneven. We propose a self-learning based adaptive clustering scheme to solve the poor performance problems of the existing clustering in case of Hot-Spot or uneven client request pattern. In the proposed scheme, requests are dynamically redistributed to the other servers if some servers supposed to handle the requests become overloaded. This is done by a self-learning based method based dynamic weight adjustment algorithm so that it can be applied to a situation with even various request pattern or a cluster of hosts with different performance. We performed experiments in a clustering environment with 16 PCs and a load balancer. Experimental results show the 54.62% performance improvement of the proposed schemes compared to the existing schemes.