Enhancement of Web Proxy Caching Using Random Forest Machine Learning Technique
Sagayaraj Francis, Paul Memorial · 2014
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.