NeTS: Small: RUI: Automating Active Measurement Metadata Collection and Analysis

NeTS：小型：RUI：自动化主动测量元数据收集和分析

• 批准号: 1814537
• 负责人: Joel Sommers
• 金额: $ 14.62万
• 依托单位: Colgate University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1814537&HistoricalAwards=false
• 关键词: NeTS; Small; RUI; Automating; Active

The extraordinary growth of Internet connectivity and bandwidth has helped to fuel economic activity, educational opportunities, and social connections around the world. The network research community has continued to play a critical role in assessing current Internet performance and in using measurements of network performance and behavior toward the design of new and improved infrastructure, protocols, and applications. Even with advances in network performance measurement techniques over the years, however, it is easily possible for measurements to exhibit bias or otherwise be error-ridden. The primary objective of this project is to develop methods for capturing important contextual information about a network performance measurement experiment and to create algorithms to assess whether the measurements exhibit bias or other problems. Extensive experiments will be carried out in both a campus network and in the wide-area Internet to evaluate the tools developed through this project. The outcomes of this project will potentially improve the quality and reproducibility of performance measurements produced through efforts such as the Federal Communication Committee?s (FCC's) Measuring Broadband America as well as other prominent network measurement efforts. Empirical research in the Internet is fraught with challenges. Perhaps chief among these is that local environmental conditions (e.g., central processing unit (CPU) load or network load) can introduce unexpected bias or artifacts that can lead to poor measurement quality and erroneous conclusions. This project will investigate and develop new methods for automatic collection of metadata and calibration data for network measurement experiments and create algorithms for detecting and potentially correcting for bias in the measurement data. Collection of metadata for measurement methods that target all layers of the protocol stack will be addressed through this project, such as latency (ping), routing (traceroute), TCP throughput, and application-level performance measurements. The tools developed through this project will be deployed for extensive experimentation in a campus network as well in a widely-used platform to evaluate and improve their performance and operational capabilities. The tools will be made openly available to the research community and will be designed for lightweight operation on a variety of target computing platforms and thus be broadly deployable. Best-practices documentation will be created and published to facilitate optimal use of the tools and algorithms developed through our research. The methods, algorithms, and software developed through this project will provide Internet measurement researchers with a toolbox for collecting and analyzing metadata and the associated data from experiments. These techniques will potentially improve confidence in the use of network measurement data not only by the researcher who captures the data, but by others who may eventually use the data. They will also enhance scientific reproducibility by providing portable, independent, and scalable methods to capture important contextual information about experiments. The tools and methods developed through this project will potentially improve the quality of measurements and outcomes produced through efforts such as the FCC's Measuring Broadband America, which directly serves the national interest through the progress of science. Lastly, undergraduate students will be involved in all stages of the research, providing them with invaluable extracurricular experiences, and inspiring them to pursue careers in science and engineering, a goal which also serves the national 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.

互联网连通性和带宽的非凡增长有助于推动世界各地的经济活动，教育机会和社会联系。 网络研究社区在评估当前的互联网性能以及使用网络性能和行为的测量方面继续发挥着关键作用，以设计新的和改进的基础架构，协议和应用程序。 但是，即使多年来网络性能测量技术的进步，测量很容易表现出偏见或以其他方式出现错误。 该项目的主要目的是开发用于捕获有关网络性能测量实验的重要上下文信息的方法，并创建算法以评估测量结果是否表现出偏见或其他问题。 将在校园网络和广阔的互联网上进行广泛的实验，以评估通过该项目开发的工具。 该项目的结果有可能提高通过努力（例如，衡量宽带美国）以及其他著名的网络测量工作等努力产生的绩效测量的质量和可重复性。 互联网中的实证研究充满了挑战。 也许其中的主要是，当地的环境条件（例如中央处理单元（CPU）负载或网络负载）可能会引入意外的偏见或工件，从而导致测量质量差和错误的结论。 该项目将研究并开发新方法，以自动收集元数据和校准数据，以进行网络测量实验，并创建用于检测和可能纠正测量数据中偏差的算法。 用于测量方法的元数据的收集将通过该项目来解决，这些方法将通过该项目来解决，例如延迟（PING），路由（Traceroute），TCP吞吐量和应用程序级别的性能测量。 通过该项目开发的工具将在校园网络中进行广泛的实验以及广泛使用的平台，以评估和提高其性能和运营能力。 这些工具将公开向研究社区提供，并将设计用于各种目标计算平台上的轻巧操作，因此可以广泛地部署。 将创建和发布最佳实践文档，以促进通过我们的研究开发的工具和算法的最佳使用。 通过该项目开发的方法，算法和软件将为互联网测量研究人员提供用于收集和分析元数据的工具箱以及实验中的相关数据。 这些技术不仅可以通过捕获数据的研究人员来提高对网络测量数据的信心，而且还可以最终使用数据。 它们还将通过提供便携式，独立和可扩展的方法来捕获有关实验的重要上下文信息，从而提高科学可重复性。 通过该项目开发的工具和方法将有可能提高通过诸如FCC的测量宽带美国之类的努力产生的测量和成果的质量，该宽带美国通过科学的进步直接为国家利益服务。 最后，本科生将参与研究的各个阶段，为他们提供了宝贵的课外经验，并激励他们从事科学和工程领域的职业，这一目标也符合国家利益。该奖项反映了NSF的法规使命，并被认为是通过基金会的知识绩效和广泛的crietia crietia criter criter criter criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria crietia crietia criteria cristia奖。

项目成果

Reassessing the Constancy of End-to-End Internet Latency

• 发表时间: 2021
• 作者: Lily Davisson;Joakim Jakovleski;Nhiem Ngo;Chau Minh Pham;J. Sommers

Bokeh: obfuscating physical infrastructure maps

• DOI: 10.1145/3356994.3365501
• 发表时间: 2019-11
• 期刊: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising
• 作者: Yugali Gullapalli;Jeremy Koritzinsky;Meenakshi Syamkumar;P. Barford;Ramakrishnan Durairajan;J. Sommers

ELF: High-Performance In-band Network Measurement

ELF：高性能带内网络测量

• 发表时间: 2021
• 期刊: IFIP Network Traffic Measurement and Analysis Conference
• 作者: Sommers, Joel;Durairajan, Ramakrishnan
• 通讯作者: Durairajan, Ramakrishnan