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

及时提取用户分析的能力对于众多在线应用程序至关重要。这包括广告和金融应用，但也包括越来越多的在线机器学习系统，这些系统依赖分析触发进行及时计算。虽然众所周知，在线数据在到达时是最有价值的，但所有现有的流分析系统都假定到达时间是用户请求到达客户端与之通信的服务器时。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及早捕获并分析处理用户请求。为了实现基于边缘的流媒体分析，该项目提出了语义Cookie，即由服务器设置并保存在客户端的加密数据结构。与最先进的网络Cookie相反，该项目的目标是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。本研究的主要研究方向是：(1)修改在线流媒体分析系统，为提出的系统奠定基础；(2)设计、实现和评估所提出的流媒体分析系统；(3)研究分析系统的安全性、保密性和可靠性。这可以导致越来越多的应用程序基本上依赖于由所提议的系统实现的快速分析触发器。该项目的洞察力、理论和算法将帮助网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，以便与云提供商和应用程序开发商合作，利用他们与客户的接近并从中获利。所有与该项目相关的数据，包括测量数据、代码和结果，都将在http://networks.cs.northwestern.edu/edge-analytics/.上公开和公开提供该网站将在项目期间维护，所有数据在项目完成后至少五年内仍可从网站下载。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。

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.