Optimizing web servers using page rank prefetching for clustered accesses

This thesis presents a Page Rank based prefetching technique for accesses to web page clusters. The approach uses the link structure of a requested page to determine the “most important ” linked pages and to identify the page(s) to be prefetched. The underlying premise of our approach is that in the case of cluster accesses, the next pages requested by users of the web server are typically based on the current and previous pages requested. Furthermore, if the requested pages have a lot of links to some “important” page, that page has a higher probability of being the next one requested. An experimental evaluation of the prefetching mechanism is presented using real server logs. The results show that the Page-Rank based scheme does better than random prefetching for clustered accesses, with hit rates of 90 % in some cases. ii Acknowledgements I would like to thank my parents Yuri and Natalie and my fiancée Rowena for their love and support during my studies in graduate school. I am grateful to my advisor Professor Manish Parashar for invaluable guidance, encouragement and support throughout my stay at Rutgers.