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pART2: using adaptive resonance theory for web caching prefetching

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A Correction to this article was published on 13 October 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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Change history

  • 13 October 2017

    In the original publication, the second name of the fourth author was incorrect. It should read as ‘Jimmy Xiangji Huang’. The original publication of the article has been updated to reflect the change.

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Acknowledgements

The authors thank the editor and the anonymous reviewers for their detailed and constructive comments that helped to improve the quality of the article. This research was supported by the Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC). It was also supported by an NSERC CREATE award in ADERSIM (http://www.yorku.ca/adersim) and the York Research Chairs (YRC) program. Support from the Information Technology of Trent University in providing data sets is acknowledged.

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Authors and Affiliations

  1. Trent University, Peterborough, ON, K9L 0G2, Canada

    Wenying Feng

  2. Netex Enterprises Inc., Toronto, ON, M4A 1H2, Canada

    Toufiq Hossain Kazi

  3. Central Michigan University, Mount Pleasant, MI, 48859, USA

    Gongzhu Hu

  4. York University, Toronto, ON, M3J 1P3, Canada

    Jimmy Xiangji Huang

Authors
  1. Wenying Feng
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  2. Toufiq Hossain Kazi
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  3. Gongzhu Hu
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  4. Jimmy Xiangji Huang
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Corresponding author

Correspondence to Wenying Feng.

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The authors declare that they have no competing interests.

Additional information

The original version of this article has been updated: the second name of the fourth author was incorrect. It should read as ‘Jimmy Xiangji Huang’.

A correction to this article is available online at https://doi.org/10.1007/s00521-017-3205-3.

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Feng, W., Kazi, T.H., Hu, G. et al. pART2: using adaptive resonance theory for web caching prefetching. Neural Comput & Applic 28 (Suppl 1), 1275–1288 (2017). https://doi.org/10.1007/s00521-017-3173-7

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