NeTS: Medium: Managing Datacenter Traffic Bursts, Fast and Slow

作为互联网的现代“工厂”，数据中心为我们日益数字化的世界提供了燃料。它们托管着当今从电子邮件到电子医疗解决方案的大多数应用程序。这些应用程序具有极低的延迟要求。由于网络拥塞，满足这些要求具有挑战性。传统的拥塞管理方法不能很好地适应当今日益复杂和高度动态的网络。特别是，它们无法对普遍存在的短暂网络流量高峰做出足够快的反应。这项研究的目标是通过开发数据中心网络中快速拥塞识别和管理的新范例来应对这一挑战。如果成功，该项目将提高基于云的应用的速度和效率。该项目认为，理想的拥塞控制取决于拥塞事件的时间尺度。例如，实时和本地拥塞管理技术，如分组偏转，可以有效地管理微秒级的拥塞。然而，同样的技术可能会导致不稳定、吞吐量崩溃，具有讽刺意味的是，如果在大规模拥塞期间部署，还会增加延迟。在当今动态、大规模和复杂的网络中，根据拥塞的规模进行识别和反应是具有挑战性的。为了解决这些挑战，该项目涉及两个方面的问题：(A)数据中心的拥塞事件规模是否扩大，以及频率如何？拥堵事件的结构特征和起源是什么？以及(B)我们能否设计多尺度的拥堵管理系统，并使各种技术能够互操作？该项目将构建一个全面的测量框架，系统地自动分析部署在不同主机层和网络中不同拓扑位置的多个有利位置的拥塞扩展。该项目还将开发多尺度拥堵管理技术，并引入新的抽象概念，通过根据拥堵程度将拥堵处理任务划分为不同的技术来实现这些技术的互操作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As the modern “factories” for the Internet, datacenters fuel our increasingly digital world. They host the majority of today’s applications ranging from email to e-healthcare solutions. These applications have exceedingly low latency requirements. Meeting these requirements is challenging due to network congestion. Traditional approaches for managing congestion are not well-suited for today’s increasingly complex and highly dynamic networks. In particular, they fail to react fast enough to pervasive short-lived spikes in network traffic. The goal of this research is to address this challenge by developing new paradigms for fast congestion identification and management in datacenter networks. If successful, the project will improve the speed and efficiency of cloud-based applications. It can also enable future latency-sensitive applications.The project posits that ideal congestion control depends on the time scale of the congestion event. For example, real-time and local congestion management techniques such as packet deflection can effectively manage microsecond-scale congestion. However, the same techniques can cause instability, throughput collapse and, ironically, increased latency if deployed during widescale congestion. Identifying and reacting based on the scale of congestion is challenging in today’s dynamic, large-scale, and complex networks. To resolve these challenges, this project pursues two lines of inquiry: (a) do congestion events scale in datacenters and how frequently? What are the structural properties and origins of congestion events? and (b) can we design multi-scale congestion management systems and enable various techniques to interoperate? This project will build a holistic measurement framework that systematically and automatically analyzes the scaling of congestion from multiple vantage points deployed in various layers of hosts and different topological locations in the network. This project will also develop multi-scale congestion management techniques and introduce novel abstractions that allow these techniques to interoperate by dividing the task of congestion handling among different techniques based on the scale of congestion.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.