Popularity-Based Selective Markov Model

Web prefetching is a promising solution used to reduce user's latency and improve the QOS. This paper presents a popularity-based selective Markov prefetching model for predicting the forthcoming Web pages. We make use of teh Zipf's law to model the Web objects' popularity. An experimental evaluation of the prefetching mechanism is presented using real server logs. Our trace-driven simulation results show that the popularity-based selective. Markov prefetching model can achieve a good hit ratio with reducing the traffic load to some degree.