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:智能工厂转型和移动机器人自动化部分受到了普遍云技术的可获得性的推动。在控制系统这样的物理网络系统中,关键部分可以迁移到云端进行卸载,实现协作处理、提高性能和生命周期管理。尽管云中的性能不确定性和中间网络中的执行和通信延迟是存在的,但目前正在研究支持对时间变化的执行和通信延迟敏感的关键应用的云原生功能服务。在本文中,我们介绍、实现并经验性地评估了一种架构,该架构成功地允许预测控制器利用云本机技术。我们的解决方案依赖于连续适应控制系统到云和中间网络的当前服务质量。正如我们的结果所示,在云端和中间网络的较差环境下,这允许控制系统在中断、嘈杂的邻居和时间变化的资源可用性下存活。如果没有此提议的解决方案,由于资源限制和响应时间不足,控制系统将会失败。此外,我们还展示了一个可以无缝切换云端的系统,并且多个使用共享资源的控制器按顺序自适应,以便没有控制器未能实现其目标。