The transformation to smart factories and the automation of mobile robotics is partly driven by a growing availability of ubiquitous cloud technologies. In cyber-physical systems, such as control systems, critical parts can be migrated to a cloud for offloading, enabling collaborative processes, improved performance, and life-cycle management. Despite the performance uncertainty in a cloud and the intermediate networks, presently, even cloud native function services are being investigated for supporting critical applications that are sensitive to time-varying execution and communication delays. In this paper, we introduce, implement, and empirically evaluate an architecture that successfully allows predictive controllers to take advantage of cloud native technology. Our solution relies on continuously adapting the control system to the present Quality of Service of the cloud and the intermediate network. As our results show, this allows a control system to survive interruptions, noisy neighbors, and time-variant resource availability. Without the proposed solution, the control system will fail due to resource constraints and insufficient response times. Further, we also show a system that can seamlessly switch between clouds and that multiple controllers using shared resources consequentially self-adapt so that no controller fails its objective.

翻译：向智能制造和移动机器人自动化的转型，部分得益于日益普及的云技术。在信息物理系统（如控制系统）中，关键组件可迁移至云端进行卸载，从而支持协作流程、性能提升及生命周期管理。尽管云端及中间网络存在性能不确定性，目前甚至云原生功能服务也在被研究用于支持对时变执行与通信延迟敏感的严苛应用。本文提出、实现并通过实验评估了一种架构，该架构成功使预测控制器能够利用云原生技术。我们的方案依赖于持续调整控制系统以适应云端及中间网络的当前服务质量。结果表明，该方案使控制系统能够在中断、干扰噪声及时变资源可用性条件下存活。若无此方案，控制系统将因资源约束及响应时间不足而失效。此外，我们还展示了一个可在云端间无缝切换的系统，且使用共享资源的多个控制器会自适应调整，从而确保无控制器偏离其目标。