Web Caching and Pre-fetching: A Data Mining Approach

摘要

With the increase in popularity of the Internet, the latency experienced by an individual, while accessing the web, is increasing. In this paper, we investigate one approach to reducing latency by increasing the hit rate for a web cache. To this effect, we developed a predictive model for pre-fetching and a modified Least Recently Used (LRU) method called AssocLRU. This paper investigates the application of a data mining technique, called Association rules to the web domain. The association rules, predict the URLs a user might reference next, and this knowledge is used in our web caching and pre-fetching model. We developed a trace driven cache simulator to compare the performance of our predictive model with the widely used replacement policy, namely, LRU. The traces we used in our experiments were the traces of Web proxy activity taken at Virginia Tech and EPA HTTP. Our results show that our predictive pre-fetching model using association rules achieves a better hit rate than both LRU and AssocLRU.