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.

工业物联网(IIoT)范式旨在创建统一的传感、计算和控制框架，将所有工业资产与信息系统和业务流程互联，并简化制造流程，实现最佳工业运营。由于IIoT应用程序-包括自动驾驶和智能高速公路、机器人制造自动化等-在严格的性能保证和可证明的健壮性方面有别于商业物联网，因此需要研究提供一个整体的资源管理框架，使其能够在存在断断续续的数据源和不可预测的系统干扰时进行有效的传感和控制操作。本项目旨在通过对三个基本问题的阐述和研究，为该框架奠定基础：1)如何在保证性能的情况下实现间断数据源和大规模高速无线控制的实时数据检索？2)如何进行动态分组调度以补偿意外的系统干扰？3)如何进行综合资源管理，以联合考虑网络和计算资源，以便在多个IIoT应用之间进行资源调度？为了从根本上改变大规模IIoT系统中资源管理的设计原则，本项目将(I)为具有间歇性数据源的IIoT系统设计新的实时数据管理算法；(Ii)开发用于多集群无线网络控制性能优化的新调度技术；(Iii)设计一个全分布式分组调度框架以应对复杂工业环境中的意外系统干扰；以及(Iv)探索新的模型和调度方法，开发一个复合资源管理框架，用于处理大规模IIoT系统中时间关键型端到端服务的异构资源调度、划分和重新配置。这些创新将使用高保真IIoT模拟工具进行验证，并部署在大学和行业共同建立的IIoT试验台上，以进行彻底的性能评估。这一拟议的资源管理框架将为研究人员和行业合作伙伴提供整体解决方案，以在大规模IIoT系统中实现可证明的性能，并支持广泛的工业应用。研究成果将被整合到康涅狄格大学的创新专业教育项目中，以创造性的方式教育当前和下一代研究人员和专业人员，以理解、欣赏和贡献快速增长和快速发展的IIoT技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

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