CAREER: A Network Motion Picture Primitive for Network Monitoring and Control

互联网是现代生活中必不可少的工具，它提供了随时随地的信息连接和访问，以及对企业和个人都很重要的许多服务。因此，作为Internet基础的网络的可靠性和可用性是至关重要的，对它们的行为有详细和全面的了解是很重要的。然而，随着网络变得越来越大、越来越复杂，对细粒度的、全网范围的行为的可见性，使网络运营商能够识别和修复问题变得越来越困难。该项目解决了这一挑战，其主要目标是开发工具和技术，使大型复杂计算机网络的行为具有前所未有的可见性。该项目的目标是开发一种新颖的原语，提供全网可见性：网络动态图像，一个快速的网络范围快照序列，其中每个快照是一组一致的局部状态观察，提供了大约一个瞬间的网络视图。现有的研究要么专注于为单个组件/路径开发更高级的度量，要么专注于从这些测量中改进对网络范围结论的推断，与之相反，所提出的原语做出了两个关键贡献：(1)它将突发和网络范围的行为视为一级公民；(2)它开发了一种抽象，用于可扩展的收集和适应网络范围行为的变化。计划中的研究有三个主要的技术组成部分：i)开发一个收集网络电影的实用框架；Ii)用于诊断、管理和修复网络系统的新型系统；Iii)用于改善大型复杂网络性能的新协议。用例将包括数据中心、边缘和物联网（IoT）网络。研究人员计划使用NSF资助的Cloudlab平台进行评估。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Internet is an essential utility for modern life, providing connectivity and access to information any time, anywhere, and to many services that are important for businesses and individuals. Thus, the reliability and availability of the networks that underlie the Internet are critical, and it is important to have a detailed and holistic understanding of their behavior. However, as networks have become larger and more complex, visibility into fine-grained, network-wide behavior that allows network operators to identify and repair problems is increasingly difficult to come by. This project addresses that challenge, with a primary goal of developing tools and techniques that enable unprecedented visibility into the behavior of large and complex computer networks.The goal of this project is the development of a novel primitive that provides whole-network visibility: the Network Motion Picture, a rapid sequence of network-wide snapshots, where each snapshot is a consistent set of local observations of state that provide a view of the network at approximately a single instant in time. In contrast to existing research that focuses on either developing more advanced metrics for individual components/paths or improving inference of network-wide conclusions from those measurements, the proposed primitive makes two key contributions: (1) it treats emergent and network-wide behavior as a first-class citizen, and (2) it develops an abstraction for scalable collection of and adaptation to changes in network-wide behavior. The planned research has three main technical components: i) the development of a practical framework for collecting Network Motion Pictures; ii) novel systems for diagnosing, managing, and repairing networked systems; iii) novel protocols for improving the performance of large, complex networks. Use cases will include data center, edge, and Internet of Things (IoT) networks. The researcher plans evaluation using the NSF=funded Cloudlab platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.