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

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.

@article{Ali2018,
  title     = {Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {8},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Waleed Ali},
  doi       = {10.14569/IJACSA.2018.090810},
  url       = {https://doi.org/10.14569/IJACSA.2018.090810}
}