Analysis of Markov model on different web Prefetching and caching schemes

The World Wide Web is growing rapidly in terms of number of users and number of web application. With this growth the response time of retrieving the web document is increasing. User´s experience on the internet can be improved by minimizing user´s web access latency. This can be done by predicting the next step taken by user towards the accessing of web page in advance, so that the predicted web page can be prefetched and cached. This prefetching and caching is useful for reducing departure of user from the website and improving the quality of service. In this paper three different schemes for web Prefetching and caching are proposed i.e. Prefetching only, Prefetching with Caching and Prefetching from Caching. Prediction of the next accessed web page for prefetching and caching is achieved by modeling the web log using Dynamic Nested Markov model. Dynamic Nested Markov model is analyzed on these three Prefetching and Caching schemes. Experiments have been conducted on real world data sets.