Sunday 21st of January 2018

Enhancement of Web Proxy Caching Using Random Forest Machine Learning Technique

Julian Benadit .P, Sagayaraj Francis .F and Nadhiya .M

The Random Forest Tree is an ensemble learning method for Web data classification. In this study, we attempt to improve the performance of the traditional Web proxy cache replacement policies such as LRU and GDSF by integrating machine learning technique for enhancing the performance of the Web proxy cache. Web proxy caches are used to improve performance of the web. Web proxy cache reduces both network traffic and response time. In the first part of this paper, a supervised learning method as Random Forest Tree classifier (RFT) to learn from proxy log data and predict the classes of objects to be revisited or not. In the second part, a Random Forest Tree classifier (RFT) is incorporated with traditional Web proxy caching policies to form novel caching approaches known as RFT-LRU and RFT-GDSF. These proposed RFT-LRU and RFT-GDSF significantly improve the performances of LRU and GDSF respectively

Keywords: Web caching, Proxy server, Cache replacement, Classification, Random Forest Tree classifier.

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Julian Benadit .P
Julian Benadit. P received the B. Tech degree in computer science engineering from Pondicherry University, Puducherry, India and the M.E. Degree in computer science engineering from Anna University, Chennai, India. He is currently pursuing the Ph.D degree in computer science engineering at Pondicherry Engineering College, Pondicherry University, Puducherry, India. Since 2006, he has been an Assistant Professor with the computer science Engineering Department, in the consortium engineering college affiliated to Pondicherry University His research interest includes web caching, web prefetching, machine learning, content distribution network. He was the Associate member of Professional society Institution of Electronics and Telecommunication Engineers (IETE), Computer Society of India (CSI) and Institution of Engineers (IE), Indian Society for Technical Education (ISTE).

Sagayaraj Francis .F
Sagayaraj Francis.F received the B.Sc, computer science in Madras university and M.Sc, computer science at St.Joseph college Trichy (Autonomous) and M.Tech degree in computer science Engineering from the Pondicherry University, Puducherry, India and he obtained his Ph.D degree in Computer Science Engineering from Pondicherry University, Puducherry, in 2008. Currently, he is working as a professor in the Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India. His research interest includes Database Management System, Knowledge and Intelligent system, Data analysis, Data Modeling.

Nadhiya .M
Nadhiya.M received Master of Computer Application (MCA) degree from Anna University, Tamilnadu, India. She is currently pursuing the M.Tech degree in computer science engineering at Dr. S.J.S Paul Memorial College of Engineering and Technology, Pondicherry University, Puducherry, India. She currently works in the domain Web caching, Machine learning

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