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 re-visited in the future is still a problem faced by existing conventional Web proxy caching techniques. In this paper, a Naïve Bayes (NB) classifier is used to enhance the performance of conventional Web proxy caching approaches such as Least-Recently-Used (LRU) and Greedy-Dual-Size (GDS). NB is intelligently incorporated with conventional Web proxy caching techniques to form intelligent and effective caching approaches known as NB-GDS, NB-LRU and NB-DA. Experimental results have revealed that the proposed NB-GDS, NB-LRU and NB-DA significantly improve the performances of the existing Web proxy caching approaches across several proxy datasets.
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