CPS: Breakthrough: Programming and Execution Environment for Geo-Distributed Latency-Sensitive Applications

CPS：突破：地理分布式延迟敏感应用程序的编程和执行环境

• 批准号: 1446801
• 负责人: Umakishore Ramachandran
• 金额: $ 45.72万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446801&HistoricalAwards=false
• 关键词: CPS; Breakthrough; Programming; Execution; Environment

The confluence of new networked sensing technologies (e.g., cameras), distributed computational resources (e.g., cloud computing), and algorithmic advances (e.g., computer vision) are offering new and exciting opportunities for solving a variety of new problems that are of societal importance including emergency response, disaster recovery, surveillance, and transportation. Solutions to this new class of problems, referred to as "situation awareness" applications, include surveillance via large-scale distributed camera networks and personalized traffic alerts in vehicular networks using road and traffic sensing. A breakthrough in system software technology is needed to meet the challenges posed by these applications since they are latency-sensitive, data intensive, involve heavy-duty processing, and must run 24x7 while dealing with the vagaries of the physical world. This project aims to make such a breakthrough, through new distributed programming idioms and resource allocation strategies. To better identify the challenges posed by situation awareness applications, the project includes experimental deployment of the new technologies in partnership with the City of Baton Rouge, Louisiana. The central activity is to develop appropriate system abstractions for design of situation awareness applications and encapsulate them in distributed programming idioms for domain experts (e.g., vision researchers). The resulting programming framework allows association of critical attributes such as location, time, and mobility with sensed data to reason about causal events along these axes. To meet the latency constraints of these applications, the project develops geospatial resource allocation mechanisms that complement and support the distributed programming idioms, extending the utility-computing model of cloud computing to the edge of the network. Since the applications often have to work with inexact knowledge of what is happening in the physical environment, owing to limitations of the distributed sensing sources, the project also investigates system support for application-specific information fusion and spatio-temporal analyses to increase the quality of results. Efforts toward development of a future cyber-physical systems workforce include creation of a new multidisciplinary curriculum around situation awareness, exploration of new immersive learning pedagogical styles, and mentoring and providing research experience to undergraduate students through research experiences and internships aimed at increasing participation of women and minorities.

新的网络传感技术的融合（例如，摄像机），分布式计算资源（例如，云计算），以及算法进步（例如，计算机视觉）正在为解决各种具有社会重要性的新问题提供新的和令人兴奋的机会，包括紧急响应、灾难恢复、监视和运输。 这类新问题的解决方案被称为“态势感知”应用，包括通过大规模分布式摄像机网络进行监控，以及使用道路和交通传感在车辆网络中进行个性化交通警报。 需要在系统软件技术方面取得突破，以应对这些应用程序带来的挑战，因为它们对延迟敏感，数据密集，涉及繁重的处理，并且必须在处理物理世界的变幻莫测时全天候运行。 该项目旨在通过新的分布式编程习惯和资源分配策略实现这样的突破。 为了更好地确定态势感知应用程序所带来的挑战，该项目包括与路易斯安那州的巴吞鲁日市合作实验性部署新技术。中心活动是为态势感知应用程序的设计开发适当的系统抽象，并将其封装在领域专家的分布式编程习惯中（例如，视觉研究人员）。 由此产生的编程框架允许关联的关键属性，如位置，时间和流动性与感测数据的原因有关的因果事件沿着这些轴。 为了满足这些应用程序的延迟限制，该项目开发了地理空间资源分配机制，补充和支持分布式编程习惯，将云计算的实用计算模型扩展到网络边缘。 由于分布式传感源的局限性，应用程序往往不得不与物理环境中发生的事情的不准确的知识，该项目还研究了系统支持特定应用的信息融合和时空分析，以提高结果的质量。努力发展未来的网络物理系统劳动力包括围绕态势感知创建新的多学科课程，探索新的沉浸式学习教学风格，并通过旨在增加妇女和少数民族参与的研究经验和实习，为本科生提供指导和研究经验。