Koren, I., Rinker, F., Meixner, K., Kröger, M., & Zeng, M. (2023). Implementing DevOps Practices in CPPS Using Microservices and GitOps. In 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1–4). IEEE. https://doi.org/10.1109/ETFA54631.2023.10275433
2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
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ISBN:
979-8-3503-3991-8
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Date (published):
2023
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Event name:
2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA)
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Event date:
12-Sep-2023 - 15-Sep-2023
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Event place:
Sinaia, Romania
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Number of Pages:
4
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Publisher:
IEEE, Piscataway
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Keywords:
Cyber-Physical Production Systems; DevOps; GitOps; Microservices
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Abstract:
As digitalization shapes Industry 4.0, integrating software engineering practices into the manufacturing domain becomes a key focus. DevOps is such a practice and methodology that enhances collaboration and automates processes in software development and IT operations. In previous work, we discussed challenges and opportunities of applying the DevOps paradigm in Cyber-Physical Production Systems (CPPS) engineering, which are complex systems that integrate physical components with computer-controlled operations. This paper instantiates our DevOps4CPPS framework, spotlighting the application of GitOps and microservices in a CPPS environment. A real-world case study from laser ultra short pulse manufacturing illustrates how the approach effectively addresses the inherent challenges of CPPS, boosting agility and operational performance, while reducing downtime. The paper provides guidance to researchers and practitioners on applying DevOps practices to CPPS, and suggests future research directions.
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Project title:
Verbesserung der Sicherheit von Informationsprozessen in Produktionssystemen: CDL SQI (Christian Doppler Forschungsgesells; Christian Doppler Forschungsgesells)
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Research Areas:
Digital Transformation in Manufacturing: 40% Computer Engineering and Software-Intensive Systems: 30% Automation and Robotics: 30%
