Compact Data Structures for Network Telemetry

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2025-02-12 DOI:10.1145/3716819
Shir Landau-Feibish, Zaoxing Liu, Jennifer Rexford
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引用次数: 0

Abstract

Collecting and analyzing of network traffic data ( network telemetry ) plays a critical role in managing modern networks. Network administrators analyze their traffic to troubleshoot performance and reliability problems, and to detect and block cyberattacks. However, conventional traffic-measurement techniques offer limited visibility into network conditions and rely on offline analysis. Fortunately, network devices—such as switches and network interface cards—are increasingly programmable at the packet level, enabling flexible analysis of the traffic in place, as the packets fly by. However, to operate at high speed, these devices have limited memory and computational resources, leading to trade-offs between accuracy and overhead. In response, an exciting research area emerged, bringing ideas from compact data structures and streaming algorithms to bear on important networking telemetry applications and the unique characteristics of high-speed network devices. In this paper, we review the research on compact data structures for network telemetry and discuss promising directions for future research.
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网络遥测的紧凑数据结构
网络流量数据的采集和分析(网络遥测)在现代网络管理中起着至关重要的作用。网络管理员通过分析网络流量来解决性能和可靠性问题,并检测和阻止网络攻击。然而,传统的流量测量技术对网络状况的可见性有限,并且依赖于离线分析。幸运的是,网络设备(如交换机和网络接口卡)在数据包级别上的可编程性越来越高,从而能够在数据包移动时对流量进行灵活的分析。然而,为了高速运行,这些设备具有有限的内存和计算资源,导致在准确性和开销之间进行权衡。作为回应,一个令人兴奋的研究领域出现了,将紧凑数据结构和流算法的思想带到重要的网络遥测应用和高速网络设备的独特特性上。本文对网络遥测中紧凑数据结构的研究进行了综述,并对未来的研究方向进行了展望。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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