Collaborative Research: CNS Core: Small: Understanding Per-Hop Flow Control

合作研究：CNS 核心：小型：了解每跳流量控制

• 批准号: 2006827
• 负责人: Mohammad Alizadeh
• 金额: $ 25万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006827&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

This research concerns how best to manage contention for data center network resources. Data centers are among the fastest growing segment of the computer industry, and networks connect the computers in a data center to allow them to communicate. Just as roads can become congested when too many people try to use them at the same time, data center networks can become congested when too many applications try to send data at the same time. Most networks today use an end to end control mechanism - as the network becomes congested, it sends signals back to the computers to slow down. It might seem that faster networks would help, but the opposite is true - the amount of network communication is also rapidly increasing, and more data can be sent before the feedback mechanism can kick in to control traffic. This project (a collaborative project between investigators at the University of Washington and Massachusetts Institute of Technology) is to explore a different approach, where feedback occurs within the network, hop-by-hop between network switches, and just for those applications that are sending too fast.The challenges for congestion control for data centers include rapidly increasing workload demand, ever faster links, small average transfer sizes, extremely bursty traffic, and limited switch buffer capacity. Existing end-to-end congestion control systems are far from optimal in these settings, and this is particularly noticeable for latency-sensitive applications. Many data center operators compensate by using priorities and/or running their networks at very low average utilization, but this raises costs without fully solving the problem. This research attempts to understand the benefits and limits of an alternative approach to congestion control for data center networks, based on per-hop flow control. The research will (i) develop a theoretical framework to quantify the difference between the two different approaches, (ii) demonstrate a practical implementation on modern programmable data center network switches, and (iii) understand and develop solutions for the engineering challenges of using per-hop flow control in data centers.If successful, the research will help enable an emerging class of latency-sensitive applications to be deployed within and across data centers and at lower cost, for bursty traffic patterns and emerging very high bandwidth networks being developed in industry. Data center network technologies are rapidly evolving, and so a key aspect of this research is to develop materials to help train undergraduate and graduate students for the challenges that latency-sensitive applications pose for data center networks.The project website, https://www.cs.washington.edu/homes/tom/backpressure/, contains copies of all project papers, presentations, source code, simulations, experimental results, and teaching materials. Additional material will be placed there as the project progresses, and will be maintained for a minimum of ten years after the completion of the project.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.

这项研究涉及如何最好地管理数据中心网络资源的争用。数据中心是计算机行业增长最快的部分之一，网络连接数据中心中的计算机以允许它们进行通信。正如当太多人尝试同时使用道路时，道路会变得拥堵一样，当太多应用程序尝试同时发送数据时，数据中心网络也会变得拥堵。当今大多数网络都使用端到端控制机制 - 当网络变得拥塞时，它会将信号发送回计算机以减慢速度。似乎更快的网络会有所帮助，但事实恰恰相反 - 网络通信量也在迅速增加，并且在反馈机制启动以控制流量之前可以发送更多数据。该项目（华盛顿大学和麻省理工学院研究人员之间的合作项目）旨在探索一种不同的方法，其中反馈发生在网络内，在网络交换机之间逐跳，并且仅针对那些正在发送信息的应用程序。数据中心拥塞控制面临的挑战包括快速增加的工作负载需求、更快的链路、较小的平均传输大小、极其突发的流量以及有限的交换机缓冲区容量。现有的端到端拥塞控制系统在这些设置中远非最佳，这对于延迟敏感的应用程序尤其明显。许多数据中心运营商通过使用优先级和/或以非常低的平均利用率运行网络来进行补偿，但这会增加成本，而无法完全解决问题。本研究试图了解基于每跳流量控制的数据中心网络拥塞控制替代方法的优点和局限性。该研究将（i）开发一个理论框架来量化两种不同方法之间的差异，（ii）演示现代可编程数据中心网络交换机的实际实现，以及（iii）了解和开发解决方案，以应对使用每个方法的工程挑战-数据中心中的跳流控制。如果成功，该研究将有助于以较低的成本在数据中心内部和跨数据中心部署新兴的延迟敏感应用程序，以应对突发流量模式和正在开发的新兴极高带宽网络。行业。数据中心网络技术正在迅速发展，因此这项研究的一个关键方面是开发材料来帮助培训本科生和研究生应对延迟敏感的应用程序给数据中心网络带来的挑战。项目网站，https://www .cs.washington.edu/homes/tom/backPressure/，包含所有项目论文、演示文稿、源代码、模拟、实验结果和教材的副本。随着项目的进展，额外的材料将被放置在那里，并在项目完成后至少保留十年。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的评估进行评估，被认为值得支持。影响审查标准。

项目成果

Scalable Tail Latency Estimation for Data Center Networks

数据中心网络的可扩展尾部延迟估计

• 发表时间: 2023
• 期刊: 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23
• 作者: Zhao, Kevin;Goyal, Prateesh;Alizadeh, Mohammad;Anderson, Thomas E.
• 通讯作者: Anderson, Thomas E.