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Intelligent Web proxy caching approaches based on machine learning techniques
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AI
DOI：10.1016/j.dss.2012.04.011.png)
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Abstract
En 中文
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.
Keywords:
Web caching
Proxy server
Cache replacement
Classification
Support vector machine
Decision tree
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