Intelligent Web proxy caching approaches based on machine learning techniques

  • Authors:
  • Waleed Ali;Siti Mariyam Shamsuddin;Abdul Samad Ismail

  • Affiliations:
  • Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Johor, Malaysia;Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Johor, Malaysia;Department of Communication and Computer Systems, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Johor, Malaysia

  • Venue:
  • Decision Support Systems
  • Year:
  • 2012

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Abstract

In this paper, machine learning techniques are used to enhance the performances of conventional Web proxy caching policies such as Least-Recently-Used (LRU), Greedy-Dual-Size (GDS) and Greedy-Dual-Size-Frequency (GDSF). A support vector machine (SVM) and a decision tree (C4.5) are intelligently incorporated with conventional Web proxy caching techniques to form intelligent caching approaches known as SVM-LRU, SVM-GDSF and C4.5-GDS. The proposed intelligent approaches are evaluated by trace-driven simulation and compared with the most relevant Web proxy caching polices. Experimental results have revealed that the proposed SVM-LRU, SVM-GDSF and C4.5-GDS significantly improve the performances of LRU, GDSF and GDS respectively.