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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 8, 2018.
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.
Waleed Ali, “Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches” International Journal of Advanced Computer Science and Applications(IJACSA), 9(8), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090810
@article{Ali2018,
title = {Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090810},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090810},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {8},
author = {Waleed Ali}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.