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NSF-BSF: CNS Core: Small: Machine Learning for Real-Time Network Rate Control

NSF-BSF：CNS 核心：小型：用于实时网络速率控制的机器学习

基本信息

批准号：
2008971
负责人：
Philip Godfrey
金额：
$ 50万
依托单位：
University of Illinois at Urbana-Champaign
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 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008971&HistoricalAwards=false
关键词：
NSF BSF CNS Core Small

项目摘要

Any time an application uses the Internet, behind the scenes, rate control algorithms decide how quickly to transmit data. These algorithms have a critical task; sending too slowly causes delay or reduced video quality, and sending too quickly causes congestion that impacts both the sender and other users. Rate control is a persistent challenge in networking, as it has to deal with a wide range of dynamic environments while making millisecond-level decisions with limited information. This project is developing new approaches to rate control based on an area of machine learning known as reinforcement learning, leading to potential improvements in performance and functionality.At a high level, the project seeks to develop a fundamental understanding of the use of reinforcement learning for rate control, and apply that understanding to the areas of adaptive bitrate (ABR) video as commonly used in modern web-based video, and to transport layer congestion control, such as the Transmission Control Protocol (TCP). The work will begin with algorithmic foundations by exploring what level of complexity of learning algorithm (ranging from bandit algorithms to deep neural networks) is necessary to achieve high performance. Next, the project will broaden the semantics of inputs and outputs of rate control, including a scavenger rate control protocol and an improved multipath TCP. Finally, the project will use novel automated methods to improve the robustness of rate control protocols in unexpected environments.The results of this project can offer significant performance improvement for deployed protocols, which is of increasing need as modern and emerging applications have ever more demanding network requirements. For example, low latency communication and high quality real-time video are valuable for interactive conferencing, augmented and virtual reality, Internet of Things, edge computing, and more. The project also plans to provide research opportunities for underrepresented groups.The results of this project, including papers and open-source code, will be available at http://pccproject.net and at the code repository, https://github.com/PCCprojectThis 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.

任何时候应用程序使用互联网，在幕后，速率控制算法决定传输数据的速度。这些算法有一个关键任务；发送太慢会导致延迟或降低视频质量，而发送太快会导致拥塞，从而影响发送方和其他用户。速率控制在网络中是一个持续的挑战，因为它必须处理各种动态环境，同时在有限的信息下做出毫秒级的决策。该项目正在开发基于强化学习的机器学习领域的速率控制新方法，从而提高性能和功能。在高层次上，该项目旨在发展对使用强化学习进行速率控制的基本理解，并将这种理解应用于现代基于web的视频中常用的自适应比特率（ABR）视频领域，以及传输层拥塞控制，如传输控制协议（TCP）。这项工作将从算法基础开始，探索学习算法的复杂程度（从强盗算法到深度神经网络）是实现高性能所必需的。接下来，该项目将扩大速率控制输入和输出的语义，包括清道夫速率控制协议和改进的多路径TCP。最后，该项目将使用新颖的自动化方法来提高意外环境下速率控制协议的鲁棒性。该项目的结果可以为部署的协议提供显著的性能改进，随着现代和新兴应用程序对网络的要求越来越高，这是越来越需要的。例如，低延迟通信和高质量实时视频对于交互式会议、增强现实和虚拟现实、物联网、边缘计算等都很有价值。该项目还计划为代表性不足的群体提供研究机会。该项目的成果，包括论文和开源代码，将在http://pccproject.net和代码库中提供，https://github.com/PCCprojectThis奖反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DChannel: Accelerating Mobile Applications With Parallel High-bandwidth and Low-latency Channels

DChannel：通过并行高带宽和低延迟通道加速移动应用程序

DOI：
发表时间：
2023
期刊：
Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation
影响因子：
0
作者：
Sentos, W.;Chandrasekaran, B.;Godfrey, P. B.;Hassanieh, H.;Maggs, B.
通讯作者：
Maggs, B.

Toward greater scavenger congestion control deployment: implementations and interfaces

实现更好的清道夫拥塞控制部署：实现和接口

DOI：
10.1145/3472305.3472323
发表时间：
2021
期刊：
ACM/IRTF Applied Networking Research Workshop (ANRW
影响因子：
0
作者：
Meng, Tong;Cai, Christopher;Godfrey, Brighten;Schapira, Michael
通讯作者：
Schapira, Michael

MPCC: online learning multipath transport

DOI：
10.1145/3386367.3433030
发表时间：
2020-11
期刊：
Proceedings of the 16th International Conference on emerging Networking EXperiments and Technologies
影响因子：
0
作者：
Tomer Gilad;Neta Rozen Schiff;Brighten Godfrey;C. Raiciu;Michael Schapira
通讯作者：
Tomer Gilad;Neta Rozen Schiff;Brighten Godfrey;C. Raiciu;Michael Schapira

PCC Proteus: Scavenger Transport And Beyond