Abstract
Web proxy caching is one of the most successful solutions for improving the performance of Web-based systems. In Web proxy caching, the popular web objects that are likely to be revisited in the near future are stored on the proxy server which plays the key roles between users and web sites in reducing the response time of user requests and saving the network bandwidth. However, the difficulty in determining the ideal web objects that will be revisited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, support vector machine (SVM) is used to enhance the performance of conventional web proxy caching such as Least-Recently-Used (LRU) and Greedy-Dual-Size-Frequency (GDSF). SVM is intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches called SVM_LRU and SVM_GDSF with better performance. Experimental results have revealed that the proposed SVM_LRU and SVM_GDSF improve significantly the performances of LRU and GDSF respectively across several proxy datasets.