A modular sampling framework for flexible traffic analysis

J. M. Silva, P. Carvalho, S. R. Lima
{"title":"A modular sampling framework for flexible traffic analysis","authors":"J. M. Silva, P. Carvalho, S. R. Lima","doi":"10.1109/SOFTCOM.2015.7314061","DOIUrl":null,"url":null,"abstract":"The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to today's networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.","PeriodicalId":264787,"journal":{"name":"2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOFTCOM.2015.7314061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

The paradigm of having everyone and everything connected in an ubiquitous way poses huge challenges to today's networks due to the massive traffic volumes involved. To turn treatable all network tasks requiring traffic analysis, sampling the traffic has become mandatory triggering substantial research in the area. Aiming at fostering the deployment and tuning of new sampling techniques, this paper presents a flexible sampling framework developed following a multilayer design in order to easily set up the characteristics of a sampling technique according to the measurement task to be assisted. The framework implementation relies on a comprehensive sampling taxonomy which identifies the granularity, selection scheme and selection trigger as the inner characteristics distinguishing current sampling proposals. As proof of concept of the versatility of this framework in testing the suitability of distinct sampling schemes, this work provides a comparative performance evaluation of classical and recent sampling techniques regarding the estimation accuracy, the volume of data involved in the sampling process and the computational weight in terms of CPU and memory usage.
灵活流量分析的模块化采样框架
每个人、每件事都以无处不在的方式连接起来,这种模式给当今的网络带来了巨大的挑战,因为涉及到巨大的流量。为了处理所有需要流量分析的网络任务,对流量进行采样已经成为一项强制性的研究。为了促进新采样技术的部署和调整,本文提出了一种灵活的采样框架,该框架遵循多层设计,以便根据要辅助的测量任务轻松设置采样技术的特征。该框架的实现依赖于一个全面的抽样分类,该分类将粒度、选择方案和选择触发器作为区分当前抽样建议的内部特征。作为该框架在测试不同采样方案适用性方面的多功能性概念的证明,本工作提供了关于估计精度,采样过程中涉及的数据量以及CPU和内存使用方面的计算权重的经典和最新采样技术的比较性能评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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