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.

Web 和视频等互联网应用程序的性能很大程度上取决于底层网络条件。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完全缓解。这是因为，考虑到网络和应用程序工作负载的变化，没有一种协议或参数设置在任何时候都效果最好。该项目的目标是开发一种新颖、统一、实用的建模框架，能够准确预测不同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种新的拥塞感知 TCP（传输）来实现其既定目标。控制协议）性能模型，然后扩展该模型以涵盖应用程序层。在 TCP 层，性能将通过分析进行建模，但将通过根据经验观察到的网络拥塞与传输速率之间的关系来增强性能。该模型将使用预测和聚类技术，根据目标流附近现有或历史流的网络信息引导模型参数。对于 Web，该 TCP 模型将扩展为预测页面加载时间，而对于视频应用，TCP 吞吐量预测将用于驱动比特率选择算法。这项研究针对的是广泛的人群，因为视频和 Web 流量几乎构成了所有内容。互联网上的流量。这项工作通过提供系统地了解不同协议对提高性能的效果的方法，对初创公司和小型公司有利。作为持续合作的一部分，该项目的成果将用于提高发展中地区的网络性能。 PI 将向本科生和高中生介绍基于网络和视频性能、网络协议建模和测量研究的项目。 PI 在吸引高中生和本科生参与研究方面拥有成功的记录。作为该项目一部分设计的软件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保存至少5年；可能会更长，具体取决于其规模和外部兴趣。该奖项反映了 NSF 的法定使命，并且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

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

何时使用、何时不使用 BBR：实证分析与评估研究

• DOI: 10.1145/3355369.3355579
• 发表时间: 2019-10
• 期刊: IMC '19: Proceedings of the Internet Measurement Conference
• 作者: Cao, Yi;Jain, Arpit;Sharma, Kriti;Balasubramanian, Aruna;Gandhi, Anshul
• 通讯作者: Gandhi, Anshul

BBR Bufferbloat in DASH Video

DASH 视频中的 BBR 缓冲膨胀

• DOI: 10.1145/3442381.3450061
• 发表时间: 2021-04-19
• 期刊: Proceedings of the Web Conference 2021
• 作者: Santiago Vargas;Rebecca Drucker;Aiswarya Renganathan;A. Balasubramanian;Anshul G;hi;hi
• 通讯作者: hi

ECON: Modeling the network to improve application performance

ECON：对网络进行建模以提高应用程序性能

• DOI: 10.1145/3355369.3355578
• 发表时间: 2019-10-21
• 期刊: Proceedings of the Internet Measurement Conference
• 作者: Yi Cao;Javad Nejati;A. Balasubramanian;Anshul G;hi;hi
• 通讯作者: hi

Optimizing Near-Data Processing for Spark

优化 Spark 近数据处理

• 发表时间: 2022-01
• 期刊: Proceedings of the 42nd IEEE International Conference on Distributed Computing Systems (ICDCS '22
• 作者: Rachuri, Sri Pramodh;Gantasala, Arun;Emanuel, Prajeeth;Gandhi, Anshul;Foley, Robert;Puhov, Peter;Gkountouvas, Theodoros;Lei, Hui
• 通讯作者: Lei, Hui

Are Mobiles Ready for BBR?

手机准备好使用 BBR 了吗？

• 发表时间: 2022-01
• 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