Industry 4.0 is changing data collection, storage, and analysis in industrial processes fundamentally, enabling novel applications such as flexible manufacturing of highly customized products. However, real-time control of these processes has not yet realized its full potential in using the collected data to drive further development. Indeed, typical industrial control systems are tailored to the plant they need to control, reusing, and adapting to challenge. In the past, the need to solve plant-specific problems overshadowed the benefits of physically isolating a control system from its plant. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control from plants. This separation permits us to fully realize the potential for highly distributed and transferable industrial processes even with real-time constraints arising from time-critical subprocesses. This paper explores the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms using off-the-shelf technology, that is, technologies commercially available. We present a migration architecture and show, using a specifically developed orchestration tool, that containerized applications can run on shared resources without compromising scheduled execution within given time constraints. Through latency and computational performance experiments, we explore three system setups' limits and summarize lessons learned.

中文翻译：

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通过应用程序容器进行工业控制：在 IAAS 中保持确定性

工业 4.0 正在从根本上改变工业过程中的数据收集、存储和分析，使新应用成为可能，例如高度定制化产品的灵活制造。然而，对这些过程的实时控制尚未充分发挥其利用收集到的数据推动进一步发展的潜力。事实上，典型的工业控制系统是为他们需要控制、重用和适应挑战的工厂量身定制的。过去，解决工厂特定问题的需求掩盖了将控制系统与其工厂物理隔离的好处。我们相信现代虚拟化技术，特别是应用程序容器，提供了将控制与植物分离的独特机会。这种分离使我们能够充分实现高度分布式和可转移的工业过程的潜力，即使存在由时间关键的子过程引起的实时限制。本文探讨了将工业控制软件从专用硬件转移到使用现成的裸机服务器或（边缘）云计算平台的挑战和机遇。技术，即商业上可获得的技术。我们展示了一个迁移架构，并使用专门开发的编排工具展示了容器化应用程序可以在共享资源上运行，而不会在给定的时间限制内影响计划执行。通过延迟和计算性能实验，我们探索了三种系统设置的限制并总结了经验教训。