CNS Core: Small: Dynamic and Composite Resource Management in Large-scale Industrial IoT Systems

CNS 核心：小型：大型工业物联网系统中的动态复合资源管理

• 批准号: 2008463
• 负责人: Song Han
• 金额: $ 46万
• 依托单位: University of Connecticut
• 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=2008463&HistoricalAwards=false
• 关键词: CNS; Core; Small; Dynamic; Composite

An Industrial Internet of Things (IIoT) paradigm aims at creating unified sensing, computing, and control framework to interconnect all the industrial assets with information systems and business processes and to streamline the manufacturing process and lead to optimal industrial operations. Because IIoT applications - including autonomous driving and smart highway, manufacturing automation with robots, etc. - are distinguished from commercial IoT by stringent performance guarantees and certifiable robustness, research is needed to provide a holistic resource management framework that enables effective sensing and control operations in the presence of intermittent data sources and unpredictable system disturbances. This project aims to lay the foundation for such a framework by formulating and investigating three fundamental questions: 1) How to achieve real-time data retrieval with intermittent data sources and large-scale high-speed wireless control with guaranteed performance? 2) How to perform dynamic packet scheduling to compensate for unexpected system disturbances? 3) How to perform composite resource management to jointly consider network and computing resources for resource scheduling among multiple IIoT applications? By addressing these questions, the proposed dynamic and composite resource management framework has the potential to vastly advance the adoption of IIoT technologies, accelerate the transformation of legacy communication infrastructure to advanced wireless infrastructure and boost the nation's economic growth and competitiveness.To fundamentally transform the design principles of resource management in large-scale IIoT systems, this project will (i) design novel algorithms for real-time data management in IIoT systems with intermittent data sources; (ii) develop new scheduling techniques for control performance optimization in multi-cluster wireless networks; (iii) design a fully distributed packet scheduling framework to handle unexpected system disturbances in complex industrial environments; and (iv) explore new models and scheduling methods to develop a composite resource management framework for handling heterogeneous resource scheduling, partitioning and reconfiguration for time-critical end-to-end services in large-scale IIoT systems. These innovations will be validated using high-fidelity IIoT simulation tools and deployed on university-industry co-established IIoT testbeds for thorough performance evaluation. This proposed resource management framework will provide researchers and industrial partners holistic solutions to achieve provable performance in large-scale IIoT systems and support a wide range of industrial applications. The research outcomes will be integrated into an innovative professional education program at the University of Connecticut to educate current and next-generation researchers and professionals in a creative way to understand, appreciate and contribute to the fast-growing and rapidly evolving IIoT technologies.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.

工业互联网（IIOT）范式旨在创建统一的感应，计算和控制框架，以将所有工业资产与信息系统和业务流程相互连接，并简化制造过程并导致最佳工业运营。由于IIOT应用程序（包括自动驾驶和智能高速公路，机器人的制造自动化）与商业物联网的制造自动化是通过严格的性能保证和可认证的鲁棒性区别的，因此需要进行研究以提供整体资源管理框架，以实现有效的感应和控制操作，并在存在间歇性数据源和不可估计的系统系统的情况下提供有效的感应操作。该项目旨在通过制定和调查三个基本问题来为这样一个框架奠定基础：1）如何使用间歇性数据源和大规模的高速无线控制并保证绩效来实现实时数据检索？ 2）如何执行动态数据包计划以补偿意外的系统干扰？ 3）如何执行复合资源管理以共同考虑多个IIT应用程序之间的资源调度网络和计算资源？ By addressing these questions, the proposed dynamic and composite resource management framework has the potential to vastly advance the adoption of IIoT technologies, accelerate the transformation of legacy communication infrastructure to advanced wireless infrastructure and boost the nation's economic growth and competitiveness.To fundamentally transform the design principles of resource management in large-scale IIoT systems, this project will (i) design novel algorithms for real-time data management in IIoT systems with间歇性数据源； （ii）开发新的调度技术，以在多群体无线网络中控制性能优化； （iii）设计一个完全分布的数据包调度框架，以处理复杂的工业环境中的意外系统干扰； （iv）探索新的模型和调度方法，以开发一个复合资源管理框架，用于处理大型IIOT系统中时间关键时期端到端服务的异质资源调度，分区和重新配置。这些创新将使用高保真IIT模拟工具进行验证，并部署在大学行业共同建立的IIT测试床上，以进行彻底的绩效评估。该提出的资源管理框架将为研究人员和工业合作伙伴提供整体解决方案，以在大型IIOT系统中实现可证明的绩效，并支持广泛的工业应用。研究成果将以一种创新的方式纳入康涅狄格大学的创新专业教育计划，以教育当前和下一代的研究人员和专业人士，以理解，欣赏和为快速发展且迅速发展的IIOT技术做出贡献。这项奖项反映了NSF的法定任务，并通过评估该基金会的智力效果，并通过评估了基金会的范围和广泛的范围。

项目成果

Composite Resource Scheduling for Networked Control Systems

• DOI: 10.1109/rtss52674.2021.00025
• 发表时间: 2021-09
• 期刊: 2021 IEEE Real-Time Systems Symposium (RTSS)
• 作者: Peng Wu;Chenchen Fu;Tianyu Wang;Minming Li;Yingchao Zhao;C. Xue;Song Han

Reliable Dynamic Packet Scheduling With Slot Sharing for Real-Time Wireless Networks

• DOI: 10.1109/tmc.2022.3196922
• 发表时间: 2023-11
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Tianyu Zhang;Tao Gong;Mingsong Lyu;Nan Guan;Song Han;X. Hu

Online reconfiguration of regularity-based resource partitions in cyber-physical systems

• DOI: 10.1109/rtss46320.2019.00050
• 发表时间: 2019-12
• 期刊: Real-Time Systems
• 影响因子: 1.3
• 作者: Wei-Ju Chen;Peng Wu;Pei-Chi Huang;A. Mok;Song Han