Patterns analysis and classification for Web proxy cache

Web 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 which Web objects will be re-visited in the future is still a problem faced by existing Web proxy caching techniques. In this paper, machine learning techniques are implemented to cope with the above problem. We present new intelligent approaches, which depend on the capability of Support vector machine (SVM) and decision tree (C4.5) to learn from Web proxy logs file and predict the classes of objects to be re-visited or not. Experimental results have revealed that SVM and C4.5 produce very promising performance and much faster compared to both back-propagation neural network (BPNN) and neuro-fuzzy system (ANFIS).