{"title":"Distributed Internet Paths Performance Analysis Through Machine Learning","authors":"Sarah Wassermann, P. Casas","doi":"10.23919/TMA.2018.8506572","DOIUrl":null,"url":null,"abstract":"Internet path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. In this paper we showcase Dis-NETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models.","PeriodicalId":6607,"journal":{"name":"2018 Network Traffic Measurement and Analysis Conference (TMA)","volume":" 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Network Traffic Measurement and Analysis Conference (TMA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/TMA.2018.8506572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Internet path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. In this paper we showcase Dis-NETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
