CNS Core: Medium: Design and Deployment of Bespoke Congestion Control Algorithms

CNS 核心：中：定制拥塞控制算法的设计和部署

• 批准号: 2212390
• 负责人: Justine Sherry
• 金额: $ 120万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212390&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Design; Deployment

Internet services are critical for US industry and government, the economy, health, education and social interaction. However, Internet services are not always timely or efficient, because Internet networks sometimes get congested. When Internet services transmit data, they are faced with a challenging question: how fast should they send? If they send too fast, the network will drop their data, leading to poor application performance like lost video frames or slow webpage load times. But, if they send too slowly, their data will arrive at the user slowly leading to poor application performance anyway. To make this challenging decision, applications use algorithms called Congestion Control Algorithms (CCAs) to determine the right sending rate: not too fast, and not too slow. This project is exploring new CCAs that are customized to applications; for example, applications involving video will be different than those that do not. If successful, applications performance over the Internet will be greatly enhanced. However, when several different CCAs are deployed over the Internet, they may compete with each other, and performance of some or all applications may suffer. The overarching goal is to enable application developers develop new, customized CCAs that provide better application experiences while still sharing the network equitably when different services are running at the same time. To this end, the project will design a new ultra-low latency bCCA tailored to online video game services using cross-layer optimizations that integrate bitrate adaptation with congestion control. It will perform large-scale measurement studies of the deployment of existing bCCAs, using novel machine learning approaches to identify those bCCAs that are already in use on the Internet. The project will empirically test whether new bCCAs and existing bCCAs share Internet bandwidth fairly. Finally, it will provide recommendations and guidelines for the design of future CCAs that can achieve application-performance objectives while also avoiding unfriendly behavior to competing Internet connections.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.

互联网服务对美国工业和政府、经济、健康、教育和社会互动至关重要。 然而，互联网服务并不总是及时或高效的，因为互联网网络有时会拥塞。当互联网服务传输数据时，它们面临着一个具有挑战性的问题：它们应该以多快的速度发送？如果他们发送得太快，网络将丢弃他们的数据，导致应用程序性能不佳，如丢失视频帧或网页加载速度缓慢。但是，如果他们发送得太慢，他们的数据将缓慢地到达用户，无论如何都会导致应用程序性能低下。为了做出这一具有挑战性的决定，应用程序使用称为拥塞控制算法（CCA）的算法来确定正确的发送速率：不要太快，也不要太慢。该项目正在探索新的CCA，这些CCA是为应用程序定制的;例如，涉及视频的应用程序将与不涉及视频的应用程序不同。如果成功，互联网上的应用程序性能将大大提高。然而，当几个不同的CCA部署在互联网上，他们可能会相互竞争，一些或所有应用程序的性能可能会受到影响。总体目标是使应用程序开发人员能够开发新的定制CCA，提供更好的应用程序体验，同时在不同服务同时运行时仍然公平地共享网络。为此，该项目将设计一种新的超低延迟bCCA，用于使用跨层优化的在线视频游戏服务，将比特率自适应与拥塞控制相结合。它将对现有bCCA的部署进行大规模测量研究，使用新的机器学习方法来识别已经在互联网上使用的bCCA。 该项目将实证测试新的bCCA和现有的bCCA是否公平地共享互联网带宽。 最后，它将为未来CCA的设计提供建议和指导方针，这些CCA可以实现应用程序性能目标，同时避免对竞争Internet连接的不友好行为。该奖项反映了NSF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。

项目成果

CC-fuzz: genetic algorithm-based fuzzing for stress testing congestion control algorithms

CC-fuzz：基于遗传算法的模糊测试，用于压力测试拥塞控制算法

• DOI: 10.1145/3563766.3564088
• 发表时间: 2022
• 期刊: HotNets '22: Proceedings of the 21st ACM Workshop on Hot Topics in Networks
• 作者: Ray, Devdeep;Seshan, Srinivasan
• 通讯作者: Seshan, Srinivasan

Automating network heuristic design and analysis

• DOI: 10.1145/3563766.3564085
• 发表时间: 2022-11
• 期刊: Proceedings of the 21st ACM Workshop on Hot Topics in Networks
• 作者: Anup Agarwal;V. Arun;Devdeep Ray;R. Martins;S. Seshan