CNS Core: Small: Enabling Streaming Analytics at the Network Edge

CNS 核心：小型：在网络边缘启用流分析

• 批准号: 2226107
• 负责人: Aleksandar Kuzmanovic
• 金额: $ 40万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226107&HistoricalAwards=false
• 关键词: CNS; Core; Small; Enabling; Streaming

The ability to extract user analytics in a timely manner is of critical importance for numerous online applications. This includes advertising and financial applications, but also a growing number of online machine-learning systems, which depend on analytics triggers to conduct a timely computation. While it is well-established that online data is most valuable at the time of arrival, all existing streaming analytics systems assume that the time of arrival is when a user request reaches a server that a client communicates to. The key question the project aims to answer is if one can utilize the edge network infrastructure to early capture and analytically process user requests.To enable edge-based streaming analytics, the project proposes semantic cookies, encrypted data structures set by the server and then kept at the client. Contrary to the state-of-the-art web cookies, the project aims to plant semantic user information directly into the cookies themselves. This enables collaborating edge components to analytically process the user requests. The project proposes a theory and systems to make edge-based online streaming analytics a reality. The main research thrusts of the proposed research are (1) revise the online streaming analytics to lay down the foundation for the proposed system, (2) design, implement, and evaluate the proposed analytics system, and (3) investigate its security, privacy, and reliability.This project has the potential to make a significant impact by enabling an edge-based streaming analytics service with performance properties unattainable by existing systems. This can provide a lead to a growing number of applications that fundamentally depend on fast analytical triggers enabled by the proposed system. This project’s insights, theory, and algorithms will help in-network streaming analytics become a mainstream service offered by network operators. Importantly, the project opens the doors to edge Internet Service Providers and Content Distribution Networks to leverage and monetize their proximity to clients, in collaboration with cloud providers and application developers.All the data associated with this project, including measurement data, code, and results, will be made publicly and openly available at http://networks.cs.northwestern.edu/edge-analytics/. This website will be maintained for the duration of the project, and all the data will remain available for download from the website for at least five years after the project is completed.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.

及时提取用户分析的能力对于许多在线应用程序至关重要。这包括广告和金融应用，以及越来越多的在线机器学习系统，这些系统依赖于分析触发器来进行及时的计算。虽然众所周知，在线数据在到达时最有价值，但所有现有的流媒体分析系统都假设到达时间是用户请求到达客户端通信的服务器时。该项目旨在回答的关键问题是，是否可以利用边缘网络基础设施来早期捕获和分析处理用户请求。为了实现基于边缘的流媒体分析，该项目提出了语义cookie，由服务器设置加密数据结构，然后保存在客户端。与最先进的网络cookie相反，该项目旨在将语义用户信息直接植入cookie本身。这使得协作边缘组件能够分析地处理用户请求。该项目提出了一种理论和系统，使基于边缘的在线流媒体分析成为现实。拟议研究的主要研究方向是（1）修改在线流媒体分析，为拟议系统奠定基础，（2）设计，实施和评估拟议分析系统，以及（3）调查其安全性，隐私，和可靠性。该项目有可能通过实现边缘-基于流媒体分析服务，具有现有系统无法达到的性能属性。这可以导致越来越多的应用程序从根本上依赖于所提出的系统启用的快速分析触发器。该项目的见解，理论和算法将帮助网络内流媒体分析成为网络运营商提供的主流服务。重要的是，该项目为边缘互联网服务提供商和内容分发网络打开了大门，通过与云提供商和应用程序开发人员的合作，利用他们与客户的接近度并从中获利。与该项目相关的所有数据，包括测量数据，代码和结果，将在http://networks.cs.northwestern.edu/edge-analytics/上公开和开放。该网站将在项目期间维护，项目完成后至少五年内，所有数据仍可从网站下载。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。