CNS Core: Small: Expedient Computing: A Framework for Energy-Efficient and Timely Computing at the Edge

CNS 核心：小型：便捷计算：边缘节能和及时计算的框架

• 批准号: 2219180
• 负责人: Suresh Subramaniam
• 金额: $ 58.47万
• 依托单位: George Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219180&HistoricalAwards=false
• 关键词: CNS; Core; Small; Expedient; Computing

Emerging and future cyber-physical system applications demand timely processing at the network edge of large volumes of fresh data from sensor nodes, which may have limited processing ability and energy budgets. The sheer variety of envisioned applications and their requirements call for new performance metrics and strategies for balancing freshness of information, timeliness and accuracy of results, and the energy consumed in producing those results. This project proposes and develops a new holistic framework called Expedient Computing, in which results are produced as quickly as needed, with as much precision/accuracy as needed, using information that is as fresh as needed. The specific research objectives of the project include: (i) characterizing fundamental tradeoffs in expedient computing, (ii) exploration of emerging hardware designs in edge servers for expedient computing, (iii) developing an integrated optimization framework for expedient computing, and (iv) demonstrating expedient computing principles. This cross-disciplinary project will draw upon expertise in hardware architecture, network optimization and performance analysis, algorithms, and fundamental limits.Due to the inexorable desire to make the world smarter, millions upon millions of sensors are being deployed worldwide. The successful realization of envisioned applications such as connected autonomous vehicles and remote robotic surgery will critically depend on the timely extraction of actionable intelligence from sensor data. This project lays the groundwork for doing so while balancing the limitations in energy and processing capabilities and exploring the tradeoffs between them. The outcomes of this research project can offer significant performance improvements by maximizing precision, timeliness, energy efficiency and their tradeoffs in broad application domains such as edge computing, mobile sensing, and Internet of Things, and catalyze the industry toward seeking energy-efficient and timely computing technologies at the edge. Project results will be disseminated via research publications, organizing workshops and tutorials in top conferences, and through training students in this crucial cross-cutting field.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.

新兴的和未来的网络物理系统应用需要在网络边缘及时处理来自传感器节点的大量新鲜数据，这些传感器节点可能具有有限的处理能力和能量预算。设想的应用程序及其要求的绝对多样性要求新的性能指标和策略，以平衡信息的新鲜度、结果的及时性和准确性以及产生这些结果所消耗的能量。该项目提出并开发了一个新的整体框架，称为权宜计算，在该框架中，可以根据需要尽快产生结果，并根据需要提供尽可能多的精确度/准确度，使用所需的最新信息。该项目的具体研究目标包括：（i）表征权宜计算中的基本权衡，（ii）探索边缘服务器中用于权宜计算的新兴硬件设计，（iii）开发用于权宜计算的集成优化框架，以及（iv）演示权宜计算原理。这个跨学科的项目将利用硬件架构、网络优化和性能分析、算法和基本限制方面的专业知识。由于让世界变得更智能的不可阻挡的愿望，全球正在部署数以百万计的传感器。互联自动驾驶汽车和远程机器人手术等预期应用的成功实现将严重依赖于从传感器数据中及时提取可操作的智能。该项目为这样做奠定了基础，同时平衡了能源和处理能力的限制，并探索了它们之间的权衡。该研究项目的成果可以通过在边缘计算、移动的传感和物联网等广泛的应用领域中最大限度地提高精度、及时性、能源效率及其权衡来提供显着的性能改进，并促进行业在边缘寻求节能和及时的计算技术。项目成果将通过研究出版物、在顶级会议上组织研讨会和教程以及通过在这一关键交叉领域培训学生来传播。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

Performance Modeling of Scheduling Algorithms in a Multi-Source Status Update System

多源状态更新系统中调度算法的性能建模

• DOI: 10.1109/isit50566.2022.9834272
• 发表时间: 2022
• 期刊: International Symposium on Information Theory
• 作者: Zou, Peng;Zhang, Jin;Subramaniam, Suresh
• 通讯作者: Subramaniam, Suresh

Optimizing the Age of Information with Segmentation and Predictive Scheduling

• DOI: 10.1109/wcnc55385.2023.10118997
• 发表时间: 2023-03
• 期刊: 2023 IEEE Wireless Communications and Networking Conference (WCNC)
• 作者: Jin Zhang;P. Zou;Suresh Subramaniam

Joint UAV Trajectory Planning, DAG Task Scheduling, and Service Function Deployment Based on DRL in UAV-Empowered Edge Computing

• DOI: 10.1109/jiot.2023.3257291
• 发表时间: 2023-07
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Xianglin Wei;Lingfeng Cai;Nan Wei;P. Zou;Jin Zhang;S. Subramaniam