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

RINGS：下一代移动环境的面向对象视频分析

• 批准号: 2147909
• 负责人: Ravi Netravali
• 金额: $ 100万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2147909&HistoricalAwards=false
• 关键词: RINGS; Object; Oriented; Video; Analytics

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.

在过去的几十年里，蜂窝网络已经发展到可以在跨越网络边缘（例如用户设备）、基站和传统云后端的日益异构的组件中提供更高的性能。这些进步背后的一个关键推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尚未充分利用这些进步。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，拟议的工作提倡重新设计 VA 软件堆栈，将 VA 操作和要求与移动边缘-云层次结构中每个平台元素带来的资源、接口和优势点明确联系起来。为了实现这一目标，该项目采用自下而上、三管齐下的方法，其中包括（1）为 VA 应用程序开发一种新的面向对象的查询语言，使上述特征变得明确且可观察，（2）利用这些功能开发一套可在不同（且受限）边缘约束下运行的 VA 计算资源感知优化，以及（3）设计一种新颖的任务放置引擎，自动 跨边缘云层次结构调整和运行 VA 应用程序。由于 VA 应用程序在交通控制、自动驾驶汽车、救灾等领域的广泛使用，拟议的研究有望使大部分人口受益。关键改进将沿着两个方向进行——(1)用自动确定VA应用程序和新兴移动网络基础设施之间适当交互的方式取代繁琐的手动分析，(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，所提出的框架将通过自动决定使用哪些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）来简化开发人员尖端 VA 应用程序的创建。另一方面，开发的系统将帮助网络运营商识别最富有成效的资源增强和有关平台的有用信息，以向应用程序元素公开。该项目还涉及外展工作，以吸引目前计算机科学领域代表性不足的人群中的学生。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，这些应用程序涵盖移动系统和网络、计算机视觉、编程语言和机器学习。作为该项目一部分设计的软件和研究成果在定期维护的公共网站上发布。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

• 发表时间: 2022-01
• 作者: Arthi Padmanabhan;Neil Agarwal;Anand Iyer;Ganesh Ananthanarayanan;Yuanchao Shu;Nikolaos Karianakis;G. Xu;R. Netravali

TOD: GPU-accelerated Outlier Detection via Tensor Operations

• DOI: 10.14778/3570690.3570703
• 发表时间: 2021-10
• 期刊: Proc. VLDB Endow.
• 作者: Yue Zhao;George H. Chen;Zhihao Jia

Collage: Seamless Integration of Deep Learning Backends with Automatic Placement

• DOI: 10.1145/3559009.3569651
• 发表时间: 2021-11
• 期刊: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques
• 作者: Byungsoo Jeon;Sunghyun Park;Peiyuan Liao;Sheng Xu;Tianqi Chen;Zhihao Jia

RECL: Responsive Resource-Efficient Continuous Learning for Video Analytics

RECL：视频分析的响应式资源高效持续学习

• 发表时间: 2023
• 期刊: 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23
• 作者: Khani, Mehrdad;Ananthanarayanan, Ganesh;Hsieh, Kevin;Jiang, Junchen;Netravali, Ravi;Shu, Yuanchao;Alizadeh, Mohammad;Bahl, Victor
• 通讯作者: Bahl, Victor