{"title":"Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement","authors":"Waleed Ali, S. Shamsuddin","doi":"10.4236/JILSA.2015.74011","DOIUrl":null,"url":null,"abstract":"One of commonly used approach to enhance \nthe Web performance is Web proxy caching technique. In Web proxy caching, \nLeast-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache \nreplacement methods, which is widely used in Web proxy cache management. LFU-DA \naccomplishes a superior byte hit ratio compared to other Web proxy cache \nreplacement algorithms. However, LFU-DA may suffer in hit ratio measure. \nTherefore, in this paper, LFU-DA is enhanced using popular supervised machine \nlearning techniques such as a support vector machine (SVM), a naive Bayes \nclassifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from \nWeb proxy logs files and then intelligently incorporated with LFU-DA to form \nIntelligent Dynamic- Aging (DA) approaches. The simulation results revealed \nthat the proposed intelligent Dynamic- Aging approaches considerably improved \nthe performances in terms of hit and byte hit ratio of the conventional LFU-DA \non a range of real datasets.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"智能学习系统与应用(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/JILSA.2015.74011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.