Generalized Sketch Families for Network Traffic Measurement

You Zhou, Youlin Zhang, Chaoyi Ma, Shigang Chen, Olufemi O. Odegbile
{"title":"Generalized Sketch Families for Network Traffic Measurement","authors":"You Zhou, Youlin Zhang, Chaoyi Ma, Shigang Chen, Olufemi O. Odegbile","doi":"10.1145/3393691.3394191","DOIUrl":null,"url":null,"abstract":"Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals.","PeriodicalId":188517,"journal":{"name":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3393691.3394191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Traffic measurement provides critical information for network management, resource allocation, traffic engineering, and attack detection. Most prior art has been geared towards specific application needs with specific performance objectives. To support diverse requirements with efficient and future-proof implementation, this paper takes a new approach to establish common frameworks, each for a family of traffic measurement solutions that share the same implementation structure, providing a high level of generality, for both size and spread measurements and for all flows. The designs support many options of performance-overhead tradeoff with as few as one memory update per packet and as little space as several bits per flow on average. Such a family-based approach will unify implementation by removing redundancy from different measurement tasks and support reconfigurability in a plug-n-play manner. We demonstrate the connection and difference in the design of these traffic measurement families and perform experimental comparisons on hardware/software platforms to find their tradeoff, which provide practical guidance for which solutions to use under given performance goals.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
网络流量测量的广义草图族
流量测量为网络管理、资源分配、流量工程、攻击检测等提供重要信息。大多数现有技术都是针对具有特定性能目标的特定应用需求。为了通过高效和面向未来的实现来支持不同的需求,本文采用了一种新的方法来建立通用框架,每个框架都适用于共享相同实现结构的一系列流量测量解决方案,为大小和分布测量以及所有流量提供高水平的通用性。这些设计支持许多性能开销权衡选项,每个数据包只需更新一次内存,平均每个流只需占用几个比特的空间。这种基于家族的方法将通过消除不同测量任务的冗余来统一实现,并以即插即用的方式支持可重构性。我们展示了这些流量测量家族设计中的联系和差异,并在硬件/软件平台上进行实验比较,以找到它们的权衡,这为在给定性能目标下使用哪种解决方案提供了实用指导。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Latency Imbalance Among Internet Load-Balanced Paths: A Cloud-Centric View Fundamental Limits of Approximate Gradient Coding Staleness Control for Edge Data Analytics vrfinder: Finding Outbound Addresses in Traceroute The Great Internet TCP Congestion Control Census
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1