CNS Core:Small: Edge Platform for Enabling Situation Awareness Applications

CNS Core：Small：支持态势感知应用的边缘平台

• 批准号: 2008368
• 负责人: Umakishore Ramachandran
• 金额: $ 49.99万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008368&HistoricalAwards=false
• 关键词: CNS; Core; Small; Edge; Platform

The ubiquity of affordable computing devices with rich sensing capabilities (e.g., smart cameras, Fitbit, Amazon echo dot, and so on) continues to create increasingly large streams of data collected from the physical world. Situation awareness applications are emerging that utilize these devices to convert sensed information into actionable knowledge. In many instances, the data gathered by these devices far exceeds their computational, storage, and network capabilities. The low-latency and high-bandwidth requirements of next generation applications also means they will quickly become constrained by limitations of the Cloud's centralized design. Edge (or Fog) computing seeks to address this problem by providing Cloud-like capabilities in geo-distributed micro datacenters across the edge of the network. However, realizing this goal is not as easy as it seems due to two challenges. First, micro datacenters are necessarily limited by physical space, and thus have only a small portion of the Cloud’s hardware capabilities. Second, at the same time, there is a sharp increase in demand to process sensed data via machine learning models to achieve rapid actuation. This proposal addresses both of these challenges by introducing a novel set of machine learning libraries (modeled as “Machine Learning as a Service”) to dynamically support different processing requirements across heterogeneous hardware, coupled with an efficient execution model for enabling a high degree of multitenancy on limited hardware resources. The proposed research is aimed at providing insight on how the incoming wave of situation awareness applications may be supported at the edge. Situations awareness applications often require tradeoffs between speed and accuracy when making decisions. At the same time, these applications are continuously evolving, creating a need to dynamically adjust the behavior of a heterogeneous edge computing infrastructure with respect to this tradeoff. In this respect neither the machine learning model used nor the hardware allocated can be static. Micro datacenters at the edge have limited hardware capacity but must provide a high degree of multitenancy to support multiple client applications simultaneously. Thus the execution framework at the edge has to be agile to ensure the most efficient use of limited hardware resources while maximizing performance and availability to different client applications. This proposal explores the research issues pertaining to platform services at the edge (akin to what Cloud computing has done for throughput-oriented applications) that caters to the needs of situation awareness application developers. Planned activities involve in situ studies via collaboration with the Georgia Tech Police Department, which creates the potential for technology transfer and direct societal benefits beyond technical accomplishments.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.

具有丰富感测能力的负担得起的计算设备的普遍存在（例如，智能摄像头、Fitbit、Amazon Echo Dot等）继续创造越来越大的从物理世界收集的数据流。 态势感知应用正在出现，它们利用这些设备将感测到的信息转换为可操作的知识。在许多情况下，这些设备收集的数据远远超出了其计算、存储和网络能力。下一代应用程序的低延迟和高带宽要求也意味着它们将很快受到云集中式设计的限制。 边缘（或雾）计算试图通过在网络边缘的地理分布式微网络中心中提供类似云的功能来解决这个问题。然而，实现这一目标并不像看起来那么容易，因为有两个挑战。首先，微计算中心必然受到物理空间的限制，因此只有一小部分云的硬件功能。其次，与此同时，通过机器学习模型处理传感数据以实现快速驱动的需求急剧增加。该提案通过引入一组新颖的机器学习库（建模为“机器学习即服务”）来动态支持跨异构硬件的不同处理要求，以及用于在有限的硬件资源上实现高度多租户的高效执行模型来解决这两个挑战。 拟议的研究旨在深入了解如何在边缘支持即将到来的态势感知应用程序。 态势感知应用程序在做出决策时通常需要在速度和准确性之间进行权衡。与此同时，这些应用程序不断发展，需要动态调整异构边缘计算基础设施的行为。在这方面，使用的机器学习模型和分配的硬件都不能是静态的。 边缘的微型数据中心的硬件容量有限，但必须提供高度的多租户以同时支持多个客户端应用程序。 因此，边缘的执行框架必须灵活，以确保最有效地使用有限的硬件资源，同时最大限度地提高不同客户端应用程序的性能和可用性。 该提案探讨了与边缘平台服务相关的研究问题（类似于云计算为面向吞吐量的应用程序所做的工作），以满足态势感知应用程序开发人员的需求。 计划的活动包括通过与格鲁吉亚技术警察局合作进行的现场研究，这为技术转让和技术成就之外的直接社会效益创造了潜力。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。

项目成果

eCloud: A Vision for the Evolution of the Edge-Cloud Continuum

eCloud：边缘云连续体演进的愿景

• DOI: 10.1109/mc.2021.3059737
• 发表时间: 2021
• 期刊: Computer
• 影响因子: 2.2
• 作者: Arulraj, Joy;Chatterjee, Abhijit;Daglis, Alexandros;Dhekne, Ashutosh;Ramachandran, Umakishore
• 通讯作者: Ramachandran, Umakishore

MicroEdge: a multi-tenant edge cluster system architecture for scalable camera processing

• DOI: 10.1145/3528535.3565254
• 发表时间: 2022-11
• 期刊: Proceedings of the 23rd ACM/IFIP International Middleware Conference
• 作者: Difei Cao;Jinsun Yoo;Zhuangdi Xu;Enrique Saurez;Harshit Gupta;T. Krishna;U. Ramachandran

Coral-Pie: A Geo-Distributed Edge-compute Solution for Space-Time Vehicle Tracking

• DOI: 10.1145/3423211.3425686
• 发表时间: 2020-12
• 期刊: Proceedings of the 21st International Middleware Conference
• 作者: Zhuangdi Xu;Harshil S. Shah;U. Ramachandran

ClairvoyantEdge: Prescient Prefetching of On-demand Video at the Edge of the Network

• DOI: 10.1109/sec54971.2022.00010
• 发表时间: 2022-12
• 期刊: 2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)
• 作者: Manasvini Sethuraman;Anirudh Sarma;Adwait Bauskar;Ashutosh Dhekne;U. Ramachandran

Creating a revolutionary academic program

创建革命性的学术项目

• DOI: 10.1145/3503915
• 发表时间: 2022
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Ramachandran, Umakishore;Galil, Zvi
• 通讯作者: Galil, Zvi