An SDN-like centralized control architecture is increasingly popular and has been widely explored in cyber-physical systems (CPS) such as manufacturing, internet-of-things, and autonomous vehicle systems for higher flexibility, programmability and scalability. However, no existing frameworks can offer domain-agnostic, easily extensible support for data-driven CPS applications. In this work, we design, implement, and open-source \textit{SDNator}, the first framework to enable extensible, data-driven control in CPS. SDNator embraces an application- and data-driven design where applications function as data consumers and producers to collectively define the workflows of the controller. SDNator also incorporates two data store backends to support both event-driven and data-driven programming patterns. Benchmarks show that SDNator is highly scalable, and delivers comparable performance to Ryu, a widely used SDN controller. Moreover, we demonstrate the capabilities and usability of SDNator through our case studies of manufacturing and networking systems. By integrating applications from respective domains, we build different ``controllers'' for different scenarios. Most notably, we leverage SDNator to implement the first digital-twin-equipped central controller for additive manufacturing fleets. We show through extensive and realistic simulations that SDNator-based scheduling can (1) significantly shorten production time and improve reliability in the presence of anomalies compared to decentralized approaches, and (2) flexibly adjust and optimize production plans upon urgent requests such as producing Personal Protective Equipment during the COVID-19 pandemic.

翻译：以SDN（软件定义网络）为代表的集中式控制架构因其高灵活性、可编程性和可扩展性，在制造业、物联网及自动驾驶系统等信息物理系统中得到广泛探索与应用。然而，现有框架均无法为数据驱动的信息物理系统应用提供跨领域通用且易于扩展的支持。本文设计、实现并开源了\textit{SDNator}——首个支持信息物理系统可扩展数据驱动控制的框架。SDNator采用应用与数据协同驱动的设计范式：应用程序作为数据消费者与生产者共同定义控制器工作流。该框架还集成了两种数据存储后端，同时支持事件驱动与数据驱动编程模式。基准测试表明，SDNator具有高度可扩展性，其性能与广泛使用的SDN控制器Ryu相当。此外，通过制造业和网络系统的案例研究，我们展示了SDNator的功能与易用性——通过集成不同领域的应用程序，可为不同场景构建定制化"控制器"。尤为重要的是，我们利用SDNator实现了首个配备数字孪生的增材制造集中控制器。大量逼真仿真实验证明：与去中心化方法相比，基于SDNator的调度策略（1）在异常情况下可显著缩短生产时间并提升可靠性；（2）能在紧急需求（如COVID-19疫情期间生产个人防护装备）时灵活调整并优化生产计划。