Industrial control via application containers: Maintaining determinism in IAAS. Issue 5 (28th June 2021)

• Record Type: Journal Article
• Authors: Hofer, Florian; Sehr, Martin; Sangiovanni‐Vincentelli, Alberto; Russo, Barbara
• Is Part Of: Systems engineering. Volume 24:Issue 5(2021)
• Page Start: 352
• Page End: 368
• Publication Date: 2021-06-28
• Subjects: container orchestration -- determinism -- IAAS -- industrial control systems -- real‐time; Systems engineering -- Periodicals; 620.0011
• DOI: 10.1002/sys.21590
• Languages: English
• ISSNs: 1098-1241

Abstract

Abstract: 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.