RINGS: Object-Oriented Video Analytics for Next-Generation Mobile Environments

在过去的几十年中，蜂窝网络已经发展为跨跨越网络边缘的日益异构的组件（例如，用户设备）到基站到传统的云后端。这些进步背后的一个关键动力是增强对边缘应用程序的支持，特别是视频分析（VA）。然而，VA应用程序目前的结构并不能充分利用这些进步。一个主要问题是缺乏开发和运行VA应用程序的结构化框架，这反过来又阻碍了利用蜂窝网络（及其边缘云层次结构）所提供的所有功能所需的部署和优化。为了解决这一限制，拟议的工作倡导重新设计的VA软件堆栈，明确地将VA操作和要求与移动的边缘云层次结构中的每个平台元素所带来的资源、接口和Vantage位置联系起来。为了实现这一目标，该项目采取了一种自下而上的三管齐下的方法，包括：（1）为VA应用程序开发一种新的面向对象的查询语言，使上述特征显式和可观察，（2）利用这些功能开发一套资源感知的VA计算优化，可以在不同的环境下运行。（和受限的）边缘约束，以及（3）设计一种新颖的任务放置引擎，其自动适应并跨边缘云层次结构操作VA应用。由于VA应用在交通控制、自动驾驶车辆、灾难救援等领域的广泛使用，拟议的研究有望使大部分人口受益。关键的改进将沿着沿着两个轴-（1）用自动确定VA应用程序和新兴的移动的网络基础设施之间的适当交互来取代艰苦的手动分析，以及（2）使边缘网络基础设施的使用民主化-并将针对两个不同的群体。一方面，拟议的框架将通过自动决定使用什么公共边缘基础设施以及如何最有效地使用它（在成本，准确性和性能方面）来简化开发人员创建尖端VA应用程序的过程。另一方面，开发的系统将帮助网络运营商确定最富有成效的资源增强和有关平台的有用信息，以暴露给应用程序元素。该项目还涉及外联工作，以吸引目前在计算机科学领域人数不足的学生。这些努力的关键是放大基于边缘的VA应用程序的跨学科性质，这些应用程序跨越移动的系统和网络、计算机视觉、编程语言和机器学习。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。

Over the past few decades, cellular networks have evolved to deliver improved performance across increasingly heterogeneous components spanning the network edge (e.g., user devices) to base stations to traditional cloud backends. A key motivator behind these advances is to enhance the support for edge applications, especially video analysis (VA). Yet VA applications are currently not structured to fully leverage those advances. A primary issue is the lack of structured frameworks to develop and run VA applications, which in turn prevents the deployment and optimizations required to take advantage of all that cellular networks (and their edge-cloud hierarchies) have to offer. To tackle this limitation, the proposed work advocates for a re-designed VA software stack that explicitly ties VA operations and requirements to the resources, interfaces, and vantage points that each platform element in a mobile edge-cloud hierarchy brings. To achieve this goal, the project takes a bottom-up, three-pronged approach that involves (1) developing a new object-oriented query language for VA applications that makes the aforementioned characteristics explicit and observable, (2) leveraging those features to develop a suite of resource-aware optimizations to VA computations that can operate under diverse (and restricted) edge constraints, and (3) designing a novel task placement engine that automatically adapts and operates VA applications across edge-cloud hierarchies.Owing to the widespread use of VA applications in sectors spanning traffic control, to autonomous vehicles, to disaster relief, the proposed research promises benefits to a large part of the population. The key improvements will come along two axes – (1) replacing painstaking manual analysis with automatic determination of the appropriate interactions between VA applications and emerging mobile networking infrastructure, and (2) democratizing the use of edge networking infrastructure – and will target two different groups. On the one hand, the proposed frameworks will simplify the creation of cutting-edge VA applications for developers by automatically deciding what public edge infrastructure to use and how to use it most effectively (in terms of cost, accuracy, and performance). On the other hand, the developed systems will assist network operators in identifying the most fruitful resource enhancements and helpful information about the platform to expose to application elements. The project also involves outreach efforts to attract students from populations currently under-represented in computer science. Key to these efforts is magnifying the interdisciplinary nature of edge-based VA applications that span mobile systems and networks, computer vision, programming languages, and machine learning. The software and research artifacts designed as part of this project are released on a regularly-maintained, public website.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.