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.

翻译：网络流量数据的收集与分析（网络遥测）在现代网络管理中扮演着关键角色。网络管理员通过分析流量来排查性能与可靠性问题，并检测及阻断网络攻击。然而，传统流量测量技术对网络状况的可见性有限，且依赖离线分析。幸运的是，交换机、网卡等网络设备在数据包层面的可编程性日益增强，使得数据包传输过程中能够就地开展灵活分析。但为实现高速运行，这些设备的存储与计算资源受限，导致准确性与开销之间需权衡取舍。为此，一个新兴的研究领域应运而生，它将紧凑数据结构和流算法领域的思路应用于重要的网络遥测应用及高速网络设备的独特特性之中。本文综述了面向网络遥测的紧凑数据结构研究，并探讨了未来具有前景的研究方向。