Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement

Waleed Ali, S. Shamsuddin
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引用次数: 6

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.
Web代理缓存替换中的智能动态老化方法
提高Web性能的常用方法之一是Web代理缓存技术。在Web代理缓存中,LFU-DA (least - frequency - used - dynamic - aging)是一种常用的代理缓存替换方法,广泛应用于Web代理缓存管理中。与其他Web代理缓存替换算法相比,LFU-DA实现了更高的字节命中率。然而,LFU-DA在命中率测量方面可能会受到影响。因此,在本文中,使用流行的监督机器学习技术(如支持向量机(SVM),朴素贝叶斯分类器(NB)和决策树(C4.5)来增强LFU-DA。SVM、NB和C4.5从Web代理日志文件中进行训练,然后与LFU-DA智能结合,形成智能动态老化(Intelligent Dynamic- Aging, DA)方法。仿真结果表明,所提出的智能动态老化方法在命中率和字节命中率方面显著提高了传统LFU-DA在一系列真实数据集上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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