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.

互联网连接和带宽的惊人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。 网络研究界在评估当前互联网性能以及使用网络性能和行为的测量来设计新的和改进的基础设施、协议和应用程序方面继续发挥着关键作用。 然而，即使随着多年来网络性能测量技术的进步，测量也很容易表现出偏差或以其他方式充满误差。 该项目的主要目标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，并创建算法来评估测量是否存在偏差或其他问题。 将在校园网和广域互联网上进行广泛的实验，以评估通过该项目开发的工具。 该项目的成果将有可能提高质量和再现性的性能测量产生的努力，如联邦通信委员会？美国联邦通信委员会（FCC）测量宽带美国以及其他突出的网络测量工作。 互联网的实证研究充满了挑战。 其中最主要的可能是当地的环境条件（例如，中央处理单元（CPU）负载或网络负载）可引入可导致差的测量质量和错误结论的意外偏差或伪像。 该项目将研究和开发用于自动收集网络测量实验的元数据和校准数据的新方法，并创建用于检测和潜在校正测量数据中偏差的算法。 针对协议栈所有层的测量方法的元数据收集将通过该项目解决，例如延迟（ping），路由（traceroute），TCP吞吐量和应用程序级性能测量。 通过该项目开发的工具将部署在校园网络以及广泛使用的平台上进行广泛的实验，以评估和提高其性能和操作能力。 这些工具将向研究界公开提供，并将设计用于在各种目标计算平台上进行轻量级操作，因此可广泛部署。 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一个工具箱，用于收集和分析元数据以及实验中的相关数据。 这些技术将潜在地提高使用网络测量数据的信心，不仅是捕获数据的研究人员，而且是最终可能使用数据的其他人。 它们还将通过提供便携、独立和可扩展的方法来捕获有关实验的重要背景信息，从而提高科学的可重复性。 通过该项目开发的工具和方法将有可能提高测量质量和通过联邦通信委员会的测量宽带美国等努力产生的结果，通过科学进步直接服务于国家利益。 最后，本科生将参与研究的各个阶段，为他们提供宝贵的课外经验，并激励他们追求科学和工程事业，这也是符合国家利益的目标。该奖项反映了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

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