One of commonly used approach to enhance the Web performance is Web proxy caching technique. In Web proxy caching, Least-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache replacement methods, which is widely used in Web proxy cache management. LFU-DA accomplishes a superior byte hit ratio compared to other Web proxy cache replacement algorithms. However, LFU-DA may suffer in hit ratio measure. Therefore, in this paper, LFU-DA is enhanced using popular supervised machine learning techniques such as a support vector machine (SVM), a naïve Bayes classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from Web proxy logs files and then intelligently incorporated with LFU-DA to form Intelligent DynamicAging (DA) approaches. The simulation results revealed that the proposed intelligent DynamicAging approaches considerably improved the performances in terms of hit and byte hit ratio of the conventional LFU-DA on a range of real datasets.
Another hybrid of LFU-DA and machine learning technique such as SVM, NB, and C4.5 proposed by Ali and Shamsuddin REF to enhance the performance of HR and BHR.
Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement
Journal of Intelligent Learning Systems and Applications
Computer Science