CRII: NeTS: Denoising Internet Delay Measurements using Weak Supervision

CRII：NeTS：使用弱监督对互联网延迟测量进行去噪

• 批准号: 1850297
• 负责人: Ramakrishnan Durairajan
• 金额: $ 17.5万
• 依托单位: University of Oregon Eugene
• 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: 2019-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850297&HistoricalAwards=false
• 关键词: CRII; NeTS; Denoising; Internet; Delay

Understanding the characteristics of the Internet is one of the key goals of Internet measurement researchers, and the service providers and content delivery networks that serve billions of users worldwide. To this end, a myriad of measurement tools and techniques have been developed. Despite these efforts, what is critically lacking is a systematic framework to interpret the results due to the presence of measurement noise. This research proposes to develop a new framework to solve this problem. The main goal of this project is to design, develop and rigorously evaluate a framework for denoising latency measurements. The objectives are to make the denoising process easy, automatic, and rapid via two key research thrusts. In the first thrust, the goal is to design and develop the framework to generate measurement noise labels (i.e., ground truth data) automatically leveraging recent advancements in machine learning. The second thrust will expand the capabilities of the framework to remove and repair the noisy measurements in an automated and rapid fashion. The efficacy of the framework will be evaluated in lab-based settings and in a real-world setting by applying it on community datasets.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.

了解互联网的特性是互联网测量研究人员以及为全球数十亿用户提供服务的服务提供商和内容交付网络的关键目标之一。为此，已经开发了无数的测量工具和技术。 尽管做出了这些努力，但由于测量噪声的存在，严重缺乏一个系统的框架来解释结果。 本研究提出了一个新的框架来解决这个问题。该项目的主要目标是设计，开发和严格评估去噪延迟测量的框架。我们的目标是通过两个关键的研究重点，使去噪过程简单，自动和快速。在第一个推力中，目标是设计和开发框架以生成测量噪声标签（即，地面实况数据）自动利用机器学习的最新进展。第二个目标将扩展框架的功能，以自动化和快速的方式去除和修复有噪音的测量结果。该框架的有效性将在基于实验室的环境中进行评估，并通过将其应用于社区数据集来在现实世界中进行评估。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Dynamic Scheduling of Approximate Telemetry Queries

• 发表时间: 2022
• 作者: Chris Misa;Walt O'Connor;Ramakrishnan Durairajan;R. Rejaie;Walter Willinger

Revisiting Network Telemetry in COIN: A Case for Runtime Programmability

• DOI: 10.1109/mnet.201.2100064
• 发表时间: 2021-09
• 期刊: IEEE Network
• 影响因子: 9.3
• 作者: Chris Misa;Ramakrishnan Durairajan;R. Rejaie;W. Willinger

Denoising Internet Delay Measurements using Weak Supervision

使用弱监督对互联网延迟测量进行去噪

• DOI: 10.1109/icmla.2019.00089
• 发表时间: 2019
• 期刊: 18th IEEE International Conference On Machine Learning And Applications (ICMLA
• 作者: Muthukumar, Anirudh;Durairajan, Ramakrishnan
• 通讯作者: Durairajan, Ramakrishnan

ARISE: A Multitask Weak Supervision Framework for Network Measurements

• DOI: 10.1109/jsac.2022.3180783
• 发表时间: 2022-08
• 期刊: IEEE Journal on Selected Areas in Communications
• 影响因子: 16.4
• 作者: Jared Knofczynski;Ramakrishnan Durairajan;W. Willinger

Can we containerize internet measurements?

• DOI: 10.1145/3340301.3341130
• 发表时间: 2019-07
• 期刊: Proceedings of the Applied Networking Research Workshop
• 作者: Chris Misa;Sudarsun Kannan;Ramakrishnan Durairajan