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Neural Computing and Applications
Published in: Neural Computing and Applications 1/2017

30-08-2017 | Original Article

pART2: using adaptive resonance theory for web caching prefetching

Authors: Wenying Feng, Toufiq Hossain Kazi, Gongzhu Hu, Jimmy Xiangji Huang

Published in: Neural Computing and Applications | Special Issue 1/2017

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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.
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Metadata
Title
pART2: using adaptive resonance theory for web caching prefetching
Authors
Wenying Feng
Toufiq Hossain Kazi
Gongzhu Hu
Jimmy Xiangji Huang
Publication date
30-08-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 1/2017
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3173-7

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