PART2: using adaptive resonance theory for web caching prefetching

Abstract

As the Web becomes the major source for information and services, fast access to relevant Web objects is a critical requirement for many applications. Various methods have been developed to achieve this goal. Web page prefetching is a commonly used technique that is highly effective in reducing user perceived delays. In this paper, we propose a new prefetching model pART2, which is based on the adaptive resonance theory (ART) for data clustering. A corresponding cache replacement policy (Probability-Based Replacement) is also proposed and developed. The new policy matches with the prefetching scheme and therefore produces a higher cache hit ratio compared with some of the traditional algorithms. To evaluate the new model, we conduct a series of experiments using data sets collected from a digital library system and Monte Carlo simulation techniques. Sensitivity of the parameters and statistical analysis are also presented. The proposed model using ART-type networks provides a promising avenue for constructing accurate caching prefetching systems that are flexible and adaptive.