Collaborative Research: IMR: MM-1A: Scalable Statistical Methodology for Performance Monitoring, Anomaly Identification, and Mapping Network Accessibility from Active Measurements

合作研究：IMR：MM-1A：用于性能监控、异常识别和主动测量映射网络可访问性的可扩展统计方法

• 批准号: 2319593
• 负责人: George Michailidis
• 金额: $ 20万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319593&HistoricalAwards=false
• 关键词: Collaborative; Research; IMR; MM; 1A

This collaborative project aims to develop and implement new statistical methodology for the detection and mapping of various aspects of delays and bandwidth quality in computer networks. Part I of the project will develop the so-called extreme delay tomography, where network links experiencing extreme delays or congestion will be identified. The goal is to do so by using only end-to-end measurements from the perimeter of the network without observing all links directly. Part II of the project will focus on combining multiple data sets and measurements to build a state-wide and potentially nation-wide map(s) of broadband accessibility at the resolution of a US-census block. The project brings together researchers from the University of Michigan, the University of California, Los Angeles, and Merit Network, with expertise in network tomography, statistics of extremes, and network measurement research. One core goal is to utilize and develop new theory on statistics of extremes that can be used to detect and identify anomalous delays in the network from end-to-end delay measurements. This will expand the fields of statistics, network tomography, and provide novel tools for situational awareness and quality of service. The second core activity of the project will contribute new statistical methodology for integrating multiple observational data sets with existing demographic covariates for the purpose of mapping broadband access. This research activity will contribute to the fields of data integration and transfer learning.The broader impacts of the project will be manifold. The direct impact of the research will be on: (i) Developing novel practical tools for network situational awareness and security; (ii) Mapping the broadband availability that will aid in understanding and identifying priority goals for public policy and also in addressing social disparities due to the broadband gap; (iii) The project will engage graduate and undergraduate students and thus contribute to training of the next generation of network measurement researchers and statisticians. The outcomes of the project will have a broader impact on the fields of statistics and network measurement research through novel theoretical, methodological, and algorithmic developments, data products and software. The latter will be disseminated to the broader research communities through journal publications, conference presentations and open-source software.An account of the research goals, achievements, and landmarks will be made publicly available on https://www.merit.edu/initiatives/#activeresearchprojects. This address will maintain pointers to data products, research reports, presentations, and events related to the sponsored research.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.

这一合作项目旨在开发和实施新的统计方法，以检测和绘制计算机网络中延迟和带宽质量的各个方面。该项目的第一部分将开发所谓的极端延迟断层扫描，其中将识别经历极端延迟或拥塞的网络链路。目标是通过仅使用来自网络周边的端到端测量来实现这一点，而不直接观察所有链路。该项目的第二部分将集中于结合多个数据集和测量结果，以美国人口普查区块的分辨率建立全州范围甚至可能全国范围的宽带接入地图(S)。该项目汇集了来自密歇根大学、加州大学洛杉矶分校和Merit Network的研究人员，他们拥有网络断层扫描、极端情况统计和网络测量研究方面的专业知识。一个核心目标是利用和发展关于极端统计的新理论，该理论可用于从端到端延迟测量中检测和识别网络中的异常延迟。这将扩大统计、网络断层成像的领域，并为态势感知和服务质量提供新的工具。该项目的第二项核心活动将有助于采用新的统计方法，将多个观测数据集与现有的人口统计协变量结合起来，以绘制宽带接入图。这项研究活动将有助于数据整合和迁移学习领域。该项目的影响将是多方面的。这项研究的直接影响将是：(I)开发新的实用工具，以提高对网络状况的认识和安全；(Ii)绘制宽带可用性图，这将有助于理解和确定公共政策的优先目标，并有助于解决由于宽带差距造成的社会差距；(Iii)该项目将吸引研究生和本科生参与，从而有助于培训下一代网络测量研究人员和统计人员。该项目的成果将通过新的理论、方法和算法发展、数据产品和软件，对统计和网络测量研究领域产生更广泛的影响。后者将通过期刊出版物、会议报告和开源软件向更广泛的研究社区传播。研究目标、成果和里程碑的描述将在https://www.merit.edu/initiatives/#activeresearchprojects.上公开提供该地址将保留与受资助研究相关的数据产品、研究报告、演示文稿和活动的指针。该奖项反映了NSF的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。