Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Abstract

This paper reports on how intelligent Greedy-Dual approaches based on supervised machine learning were used to improve the web proxy caching performance. The proposed intelligent Greedy-Dual approaches predict the significant web objects’ demand for web proxy caching using Naïve Bayes (NB), decision tree (C4.5), or support vector machine (SVM) classifiers. Accordingly, the proposed intelligent Greedy-Dual approaches effectively make the cache replacement decision based on the trained classifiers. The trace-driven simulation results indicated that in terms of byte hit ratio and/or hit ratio, the performance of each of the conventional Greedy-Dual-Size-Frequency (GDSF) and Greedy-Dual-Size (GDS) was noticeably enhanced by applying the proposed Greedy-Dual approaches on five real datasets.

Authors and Affiliations

Waleed Ali

Keywords

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  • EP ID EP375360
  • DOI 10.14569/IJACSA.2018.090810
  • Views 77
  • Downloads 0

How To Cite

Waleed Ali (2018). Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches. International Journal of Advanced Computer Science & Applications, 9(8), 76-85. https://europub.co.uk/articles/-A-375360