Based On Pruning Techniques Ppm Prediction Model

Presently caching and prefetching techniques are the primary solutions used to reduce Web access latency. Web caching technique has been widely used in different places of Internet.But as dynamic documents and personal services increase all over the world, the performance of caching deteriorates significantly. As a result, Web prefetching which is a efficient way of making up for Web caching, and the most effective method to break theupper bound of caching performance------is becoming a hotspot in Web speedup researcharea. However there are two problems to be solved before prefecting can be put into practice:One is prediction------deciding which are the most likely Web objects before the users take theaction., the other is prefetch------determining which Web objects will be prefetched and howmany Web objects can be prefetched on the basis of the current state of system. Aiming at these two problems, we propose a PPM prefetching model based on Prinnig technique in which existing prediction algorithms and prefetching control strategies are improved. Therefore, a high prediction precision can be achieved at the cost of relative low storage complexity and network traffic.First, this thesis introduces the development and the state of the art of the Internet and WWW; gives the problems Internet faced and corresponding solutions; and describes the concept, classification and structure of prefetching; then summarizes existing prediction algorithms.After a brief comment on existing prediction algorithms, our research focus on the main two Web surfing characteristics and validate these characteristics. First, Web objects consist of the high frequency objects and the low frequency ones. Zipf's 1st law can be used to depict the high frequency Web objects' popularity and for the low frequency objects we borrow Zipf's 2nd law to model. Second, viewing the Web surfing as a random walk the probability distribution of surfing depth follows a two-parameter inverse Gaussian distribution.Web surfing characteristics offer the basis for our PPM model. Base on the properties, we propose a prefetching model. By use of the pre-pruning and post-pruning technique, we rebuild the standard PPM model based on Zifp's law and Web surfing characteristics. According to Zipf's 2nd law, we remove the noisy and unpopular Web objects whose popularities are lower than or equal to the threshold value 0 for a reduced model size. By the use of Zipf's 1st law and Web surfing regularities, the model dynamically controls the depth of the PPM tree by assigning long branches to the popular URLs and shorter branches to the less popular URLs. In particular, in order to keep the scale of the model to a certain level we...