{"title":"Collaborative Traffic Measurement Using Sketches for Software Defined Networks","authors":"C. Peng, Kuo-Shiang Hsu, Pi-Chung Wang","doi":"10.1109/COMNETSAT53002.2021.9530802","DOIUrl":null,"url":null,"abstract":"In a software-defined network (SDN), statistics information is of vital importance for different applications, such as traffic engineering, flow rerouting, and attack detection. Since some resources, e.g., ternary content addressable memory, SRAM, and computing capacity, are often limited in SDN switches, traffic measurements based on flow tables or sampling become infeasible. Sketch, a hash-based data structure, monitors every packet with fixed-size memory to provide a feasible approach of traffic measurement, but there exists a tradeoff between accuracy and memory. Currently, many efficient sketch algorithms have been designed to different purposes, but they focus on the performance and applications of one single sketch. In this paper, we present a scheme to reduce redundant flow statistics collected by sketches of different SDN switches. The proposed scheme could reduce measurement overhead in sketches, obtain more accurate estimate flow size, and find the elephant flow precisely.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"77 5","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMNETSAT53002.2021.9530802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In a software-defined network (SDN), statistics information is of vital importance for different applications, such as traffic engineering, flow rerouting, and attack detection. Since some resources, e.g., ternary content addressable memory, SRAM, and computing capacity, are often limited in SDN switches, traffic measurements based on flow tables or sampling become infeasible. Sketch, a hash-based data structure, monitors every packet with fixed-size memory to provide a feasible approach of traffic measurement, but there exists a tradeoff between accuracy and memory. Currently, many efficient sketch algorithms have been designed to different purposes, but they focus on the performance and applications of one single sketch. In this paper, we present a scheme to reduce redundant flow statistics collected by sketches of different SDN switches. The proposed scheme could reduce measurement overhead in sketches, obtain more accurate estimate flow size, and find the elephant flow precisely.
在SDN (software defined network)网络中,统计信息对于流量工程、流量重路由、攻击检测等不同的应用都是非常重要的。由于一些资源,例如三元内容可寻址存储器、SRAM和计算能力,在SDN交换机中通常是有限的,因此基于流表或抽样的流量测量变得不可行的。Sketch是一种基于散列的数据结构,它使用固定大小的内存监视每个数据包,以提供一种可行的流量测量方法,但在准确性和内存之间存在权衡。目前,针对不同的目的设计了许多高效的草图算法,但它们都侧重于单个草图的性能和应用。本文提出了一种减少由不同SDN交换机草图收集的冗余流量统计的方案。该方法可以减少草图的测量开销,获得更准确的估计流大小,并精确地找到象流。