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}
}