Abstract

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 naive 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 Dynamic- Aging (DA) approaches. The simulation results revealed that the proposed intelligent Dynamic- Aging approaches considerably improved the performances in terms of hit and byte hit ratio of the conventional LFU-DA on a range of real datasets.

Highlights

  • The most popular solution for improving Web performance is a Web caching technology

  • The average improvement ratio (IR) of hit ratio (HR) achieved by support vector machine (SVM)-DA, Naïve Bayes (NB)-DA and C4.5-DA over LFU-DA in

  • Novel intelligent dynamic aging approaches based on SVM, NB and C4.5, which are known as SVM-DA, NB-DA and C4.5-DA, were suggested for making cache replacement decisions with better hit ratio

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Summary

Introduction

The most popular solution for improving Web performance is a Web caching technology. The Web caching technique is a very useful mechanism in reducing network bandwidth utilization, decreasing user-perceived delays, and reducing loads on the original servers. A multilayer perceptron network (MLP) [1], back-propagation neural network (BBNN) [2] [3], logistic regression (LR) [4], multinomial logistic regression (MLR) [5], adaptive neuro-fuzzy inference system (ANFIS) [6], and others [7]-[9] have been utilized in Web caching In most of these studies, an intelligent supervised machine learning technique was employed in Web caching individually or integrated just with Leastrecently used (LRU) algorithm. They utilize an artificial neural network (ANN) in Web proxy caching ANN training may consume more time and require extra computational overhead.

Web Proxy Cache Replacement
Supervised Machine Learning
Support Vector Machine
Naïve Bayes
Decision Tree
Intelligent Dynamic-Aging Approaches
Results and Discussion
Conclusion

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