By Topic

Popularity-based PPM: an effective Web prefetching technique for high accuracy and low storage

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xin Chen ; Dept. of Comput. Sci., Coll. of William & Mary, Williamsburg, VA, USA ; Xiaodong Zhang

Prediction by partial match (PPM) is a commonly used technique in Web prefetching, where prefetching decisions are made based on historical URLs in a dynamically maintained Markov prediction tree. Existing approaches either widely store the URL nodes by building the tree with a fixed height in each branch, or only store the branches with frequently accessed URLs. Building the popularity information into the Markov prediction tree, we propose a new prefetching model, called popularity-based PPM. In this model, the tree is dynamically updated with a variable height in each set of branches where a popular URL can lead a set of long branches, and a less popular document leads a set of short ones. Since majority root nodes are popular URLs in our approach, the space allocation for storing nodes are effectively utilized. We have also included two additional optimizations in this model: (1) directly linking a root node to duplicated popular nodes in a surfing path to give popular URLs more considerations for prefetching; and (2) making a space optimization after the tree is built to further remove less popular nodes. Our trace-driven simulation results comparatively show a significant space reduction and an improved prediction accuracy of the proposed prefetching technique.

Published in:

Parallel Processing, 2002. Proceedings. International Conference on

Date of Conference:

2002