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

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连，并简化制造流程，实现最佳工业运营。由于工业物联网应用（包括自动驾驶和智能公路、机器人制造自动化等）与商业物联网的区别在于严格的性能保证和可认证的鲁棒性，因此需要研究提供一个整体的资源管理框架，以便在存在间歇性数据源和不可预测的系统干扰的情况下实现有效的传感和控制操作。本项目旨在通过制定和研究三个基本问题为这样的框架奠定基础：1）如何实现间歇数据源的实时数据检索和大规模的高速无线控制，并保证性能？2)如何执行动态分组调度以补偿意外的系统干扰？3)如何执行复合资源管理，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？通过解决这些问题，拟议的动态和复合资源管理框架有可能大大推动工业物联网技术的采用，加速传统通信基础设施向先进无线基础设施的转型，并促进国家的经济增长和竞争力。为了从根本上改变大规模工业物联网系统中资源管理的设计原则，该项目将（i）为具有间歇性数据源的IIoT系统中的实时数据管理设计新颖的算法;（ii）为多集群无线网络中的控制性能优化开发新的调度技术;（iii）设计一个完全分布式的分组调度框架，以处理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，以开发复合资源管理框架，用于处理大规模工业物联网系统中时间关键型端到端服务的异构资源调度、分区和重新配置。这些创新将使用高保真IIoT仿真工具进行验证，并部署在大学和工业共同建立的IIoT测试平台上，以进行全面的性能评估。这一拟议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，以实现大规模IIoT系统的可证明性能，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计划中，以创造性的方式教育当前和下一代研究人员和专业人员，感谢并为快速做出贡献-该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

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.