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. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.