🤖 AI Summary
This study addresses operational inefficiencies, poor auditability, and upgrade challenges in legacy control systems of large-scale scientific facilities—such as CERN, Diamond Light Source, and Fermilab’s ACORN project. We propose a GitOps-based modernization framework that adopts Git as the single source of truth for declarative configurations and tightly integrates containerization, Infrastructure-as-Code (IaC), and cloud-native principles to establish an automated, traceable, and version-controlled control infrastructure. Notably, this work represents the first systematic integration of modern data pipelines and AI/ML capabilities into accelerator science control systems, enabling automated configuration deployment, closed-loop runtime telemetry, and intelligent anomaly detection. Empirical evaluation demonstrates significant improvements in system reliability, maintainability, and regulatory audit compliance. The approach provides a reusable technical paradigm and engineering framework for the digital transformation of big-science facilities.
📝 Abstract
GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN [1] and Diamond Light Source [2] among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab's control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.