CNS Core: Small: Significantly Improving Networked Applications using Congestion-Aware Transport Modeling

CNS 核心：小型：使用拥塞感知传输建模显着改进网络应用程序

• 批准号: 1909356
• 负责人: Aruna Balasubramanian
• 金额: $ 49.97万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909356&HistoricalAwards=false
• 关键词: CNS; Core; Small; Significantly; Improving

The performance of Internet applications such as Web and Video depends critically on the underlying network conditions. Despite several advances in networking technology, the performance problems of Internet applications have not been fully alleviated. This is because, given the variability in the network and the application workload, no one protocol or parameter setting works best at all times. The goal of this project is to develop a novel, unified, and practical modeling framework that can accurately predict the performance of applications under different network conditions and parameters.The project will achieve its stated goals by first developing a new congestion-aware TCP (Transmission Control Protocol) performance model, and then extending this model to encompass the application layer. At the TCP layer, performance will be modeled analytically, but will be augmented by empirically observed relationship between congestion in the network and the delivery rate. The model will use predictive and clustering techniques bootstrap model parameters based on network information from existing or historical flows in the vicinity of the target flow. For Web, this TCP model will be extended to predict page load times while for video applications, TCP throughput predictions will be used to drive the bitrate selection algorithm.This research targets a broad segment of the population since video and web traffic makes up almost all of the traffic on the Internet. This work is beneficial to startups and smaller companies by providing means to systematically understand the effect of different protocols to improve performance. The results of this project will be used to improve the Web performance in developing regions as part of an ongoing collaboration. The PIs will introduce projects based on Web and Video performance, network protocol modeling, and measurement studies to undergraduate and high school students. The PIs have a successful track record of engaging high-school students and undergraduates in their research.The software designed as part of this project will be released on a public site http://www.cs.stonybrook.edu/~arunab/econ. Electronic data will be archived internally. All data will be kept for at least 5 years after publication; likely longer, depending on its size and outside interest.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.

网络和视频等互联网应用程序的性能取决于基本网络条件。尽管网络技术取得了一些进步，但互联网应用程序的性能问题尚未得到充分缓解。这是因为，鉴于网络和应用程序工作负载的可变性，没有一个协议或参数设置始终效果最好。该项目的目的是开发一个新颖，统一和实用的建模框架，该框架可以准确预测在不同的网络条件和参数下应用程序的性能。该项目将通过首先开发一个新的拥塞感知的TCP（传输控制协议）性能模型来实现其既定目标，然后将此模型扩展到应用程序层。在TCP层，将通过分析进行绩效，但通过网络拥塞与输送速率之间的经验观察到的关系会增强性能。该模型将基于来自目标流附近现有或历史流的网络信息来使用预测和聚类技术引导模型参数。对于Web，该TCP模型将扩展以预测页面加载时间，而对于视频应用程序，TCP吞吐量预测将用于推动比特率选择算法。这项研究的目标是广泛的人群，因为视频和网络流量几乎构成了Internet上的几乎所有流量。这项工作对初创企业和较小的公司有益于系统地了解不同协议的效果以提高性能的效果。该项目的结果将用于改善开发区域的网络性能，作为正在进行的合作的一部分。 PI将根据网络和视频性能，网络协议建模以及测量研究介绍项目，以向本科生和高中生介绍。 PIS成功地记录了吸引高中生和本科生的研究。该软件设计为该项目的一部分，将在公共网站上发布http://www.cs.stonybrook.edu/~arunab/econ。电子数据将在内部存档。所有数据将在发布后至少保存5年；根据其规模和外部利益，可能会更长的时间。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

项目成果

ECON: Modeling the network to improve application performance

• DOI: 10.1145/3355369.3355578
• 发表时间: 2019-10
• 期刊: Proceedings of the Internet Measurement Conference
• 作者: Yi Cao;Javad Nejati;A. Balasubramanian;Anshul Gandhi

When to use and when not to use BBR: An empirical analysis and evaluation study

• DOI: 10.1145/3355369.3355579
• 发表时间: 2019-01-01
• 期刊: IMC'19: PROCEEDINGS OF THE 2019 ACM INTERNET MEASUREMENT CONFERENCE
• 作者: Cao, Yi;Jain, Arpit;Gandhi, Anshul
• 通讯作者: Gandhi, Anshul

Optimizing Near-Data Processing for Spark

• DOI: 10.1109/icdcs54860.2022.00067
• 发表时间: 2022-07
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)
• 作者: Sri Pramodh Rachuri;Arun Gantasala;Prajeeth Emanuel;Anshul Gandhi;Robert Foley;Peter Puhov;Theo Gkountouvas;H. Lei

BBR Bufferbloat in DASH Video

• DOI: 10.1145/3442381.3450061
• 发表时间: 2021-04
• 期刊: Proceedings of the Web Conference 2021
• 作者: Santiago Vargas;Rebecca Drucker;Aiswarya Renganathan;A. Balasubramanian;Anshul Gandhi

Are Mobiles Ready for BBR?

手机准备好使用 BBR 了吗？

• 发表时间: 2022
• 期刊: Proceedings of the ACM SIGCOMM Internet Measurement Conference
• 作者: Vargas, Santiago;Gunapati, Gautham;Gandhi, Anshul;Balasubramanian, Aruna
• 通讯作者: Balasubramanian, Aruna