IMR: MT: NetFlex: A Flexible Scalable & Privacy-Preserving Network Measurement Platform to Iteratively Collect Multi-modal Multi-view Network Data from Access Networks

IMR：MT：NetFlex：灵活的可扩展

• 批准号: 2323229
• 负责人: Arpit Gupta
• 金额: $ 60万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323229&HistoricalAwards=false
• 关键词: IMR; MT; NetFlex; Flexible; Scalable

As our dependence on internet-based services for work, education, healthcare, and entertainment continues to grow, the quality of broadband access becomes the critical factor determining our experiences with these services. However, this access quality in the US differs among regions, communities, and demographic groups, leading to digital inequities. Policymakers need high-fidelity data reflecting the actual state of access quality to address these inequities. Unfortunately, the existing tools gather poor-quality data that often fail to reflect users' experiences. This project aims to develop a new Internet measurement platform, NetFlex, that addresses the fundamental limitations of existing Internet measurement tools. Specifically, NetFlex facilitates the collection of high-quality network data at scale. Policymakers can use this data to make better decisions to counter digital inequities. Moreover, this project plans to deploy Netflex at campus networks, exposing students to (realistic) network measurement data and enhancing their learning experience.The project's novelty is that it facilitates the iterative collection of the "right" active or passive network measurement data from multiple vantage points, empowering the assessment of broadband access quality and diagnosing events that disrupt the user experience. Specifically, it offers a simplified user interface that displays high-fidelity reporting of the network's state and simplifies diagnosing degradation events. Under the hood, NetFlex embraces service-oriented architecture for data collection, i.e., it disaggregates different network measurement and data collection tasks into independent services. It also transforms the collected network data into holistic and standardized network data representations. These design choices enable NetFlex to strike a balance between flexibility, scalability, and privacy. Finally, the project will leverage two compelling use cases to showcase how NetFlex improves the quality of collected network measurement data and how the new data can better assess the broadband quality and diagnose degradation events.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.

随着我们对基于Internet的工作，教育，医疗保健和娱乐服务的依赖不断增长，宽带访问的质量成为确定我们在这些服务中经验的关键因素。但是，美国，社区和人口群体之间的这种访问质量有所不同，导致数字不平等。决策者需要高保真数据，以反映访问质量的实际状态以解决这些不平等。不幸的是，现有工具收集的质量不佳数据通常无法反映用户的体验。该项目旨在开发一个新的Internet测量平台NetFlex，该平台解决了现有的Internet测量工具的基本局限性。具体而言，NetFlex促进了大规模收集高质量网络数据。政策制定者可以使用这些数据来做出更好的决策来抵抗数字不平等。此外，该项目计划在校园网络中部署NetFlex，使学生接触（现实）网络测量数据并增强其学习经验。该项目的新颖性是，它有助于从多个Vantage迭代收集“正确的”主动或被动网络测量数据，从多个Vantagion获得了多个Vantagion，从而促进了宽带访问质量和诊断活动体验的评估，以破坏用户体验。具体而言，它提供了简化的用户界面，该界面显示了网络状态的高保真报告，并简化了诊断降解事件。在引擎盖下，NetFlex包含以服务为导向的数据收集的体系结构，即将不同的网络测量和数据收集任务分解为独立服务。它还将收集到的网络数据转换为整体和标准化的网络数据表示。这些设计选择使NetFlex能够在灵活性，可扩展性和隐私性之间取得平衡。最后，该项目将利用两个引人注目的用例来展示NetFlex如何提高收集到的网络测量数据的质量，以及新数据如何更好地评估宽带质量和诊断降级事件。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和宽广的影响，可以通过评估来进行评估。