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.

随着我们在工作、教育、医疗和娱乐方面对基于互联网的服务的依赖不断增长，宽带接入的质量成为决定我们使用这些服务的关键因素。然而，在美国，这种访问质量在地区、社区和人口群体之间存在差异，导致了数字不平等。政策制定者需要反映接入质量实际状况的高保真数据，以解决这些不平等问题。不幸的是，现有的工具收集的数据质量差，往往无法反映用户的体验。该项目旨在开发一个新的互联网测量平台NetFlex，解决现有互联网测量工具的基本局限性。具体而言，NetFlex有助于大规模收集高质量的网络数据。决策者可以利用这些数据做出更好的决策，以应对数字不平等。此外，该项目还计划在校园网络中部署Netflex，让学生接触到（真实的）网络测量数据，并增强他们的学习体验。该项目的新奇在于，它有助于从多个Vantage位置反复收集“正确的”主动或被动网络测量数据，从而能够评估宽带接入质量并诊断破坏用户体验的事件。具体而言，它提供了一个简化的用户界面，显示高保真的网络状态报告，并简化了诊断退化事件。NetFlex采用面向服务的数据收集架构，即：它将不同的网络测量和数据收集任务分解为独立的服务。它还将收集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，展示NetFlex如何提高收集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的法定使命，并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，被认为值得支持。