从原始数据包到出入口流量矩阵:基于分布式mapreduce的解决方案

Marco Polverini, A. Cianfrani, A. Baiocchi, M. Listanti, Valentina Salvatore
{"title":"从原始数据包到出入口流量矩阵:基于分布式mapreduce的解决方案","authors":"Marco Polverini, A. Cianfrani, A. Baiocchi, M. Listanti, Valentina Salvatore","doi":"10.1109/NOMS.2018.8406288","DOIUrl":null,"url":null,"abstract":"In this work we define a framework for the assessment of the Traffic Matrix (TM) of an Internet Service Provider (ISP) network. The solution, referred to as mrT, i) is completely distributed among network nodes, ii) is based on different Map-Reduce building blocks, iii) and is able to extract the Ingress-Egress nodes traffic relationships starting from raw traces captured on node interfaces. Each network node is able to compute a row of the TM having as input the local trace and low size files sent by other network nodes. mrT can be used for the TM computation of any packet switched network where the source and destination identifiers are unique and global, and computational resources are available in proximity of network nodes. The performance evaluation, carried out with synthetic and real traffic traces, highlight that mrT is a suitable solution for the assessment of the TM in real ISP networks with high traffic volumes. Moreover, the use of the Map-Reduce paradigm allows a reduction of more than the 50% of the execution times, with respect to an SQL-based approach.","PeriodicalId":19331,"journal":{"name":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"From raw data packets to ingress egress traffic matrix: The distributed MapReduce-based solution\",\"authors\":\"Marco Polverini, A. Cianfrani, A. Baiocchi, M. Listanti, Valentina Salvatore\",\"doi\":\"10.1109/NOMS.2018.8406288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work we define a framework for the assessment of the Traffic Matrix (TM) of an Internet Service Provider (ISP) network. The solution, referred to as mrT, i) is completely distributed among network nodes, ii) is based on different Map-Reduce building blocks, iii) and is able to extract the Ingress-Egress nodes traffic relationships starting from raw traces captured on node interfaces. Each network node is able to compute a row of the TM having as input the local trace and low size files sent by other network nodes. mrT can be used for the TM computation of any packet switched network where the source and destination identifiers are unique and global, and computational resources are available in proximity of network nodes. The performance evaluation, carried out with synthetic and real traffic traces, highlight that mrT is a suitable solution for the assessment of the TM in real ISP networks with high traffic volumes. Moreover, the use of the Map-Reduce paradigm allows a reduction of more than the 50% of the execution times, with respect to an SQL-based approach.\",\"PeriodicalId\":19331,\"journal\":{\"name\":\"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NOMS.2018.8406288\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NOMS.2018.8406288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在这项工作中,我们定义了一个评估互联网服务提供商(ISP)网络的流量矩阵(TM)的框架。该解决方案被称为mrT,它i)完全分布在网络节点之间,ii)基于不同的Map-Reduce构建块,iii)并且能够从节点接口上捕获的原始轨迹开始提取入口-出口节点流量关系。每个网络节点都能够计算具有本地跟踪和其他网络节点发送的低大小文件作为输入的TM的一行。mrT可用于任何数据包交换网络的TM计算,其中源和目的标识符是唯一的和全局的,并且计算资源在网络节点附近可用。利用合成和真实的流量轨迹进行的性能评估表明,mrT是在具有高流量的真实ISP网络中评估TM的合适解决方案。此外,与基于sql的方法相比,使用Map-Reduce范式可以减少50%以上的执行时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From raw data packets to ingress egress traffic matrix: The distributed MapReduce-based solution
In this work we define a framework for the assessment of the Traffic Matrix (TM) of an Internet Service Provider (ISP) network. The solution, referred to as mrT, i) is completely distributed among network nodes, ii) is based on different Map-Reduce building blocks, iii) and is able to extract the Ingress-Egress nodes traffic relationships starting from raw traces captured on node interfaces. Each network node is able to compute a row of the TM having as input the local trace and low size files sent by other network nodes. mrT can be used for the TM computation of any packet switched network where the source and destination identifiers are unique and global, and computational resources are available in proximity of network nodes. The performance evaluation, carried out with synthetic and real traffic traces, highlight that mrT is a suitable solution for the assessment of the TM in real ISP networks with high traffic volumes. Moreover, the use of the Map-Reduce paradigm allows a reduction of more than the 50% of the execution times, with respect to an SQL-based approach.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信