Accelerating Control Systems with GitOps: A Path to Automation and Reliability

• URL: http://arxiv.org/abs/2511.05663v1
• Date: Fri, 07 Nov 2025 19:04:05 GMT
• Title: Accelerating Control Systems with GitOps: A Path to Automation and Reliability
• Abstract summary: Poster explores how GitOps enables fully automated, auditable, and version-controlled infrastructure management.<n>Fermilab's ACORN project aims to modernize control system infrastructure and software.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production [0.7537220883022466]
  Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities.<n> deploying and maintaining ML models in operational environments remains a substantial engineering challenge.<n>We present SMOCS, a Kafka-based containerized framework that addresses this challenge through three contributions.
  arXiv  Detail & Related papers  (2026-07-02T19:43:19Z)
• AutoPipelineAI: Context-Aware CI/CD Pipeline Generation from Natural Language [0.0]
  This paper introduces the AutoPipelineAI system, which generates CI/CD pipeline configurations using natural language descriptions.<n>It integrates repository-aware analysis, automated validation systems, and a feedback mechanism that confirms the accuracy and usability of the created pipelines.
  arXiv  Detail & Related papers  (2026-06-04T19:15:07Z)
• Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC [0.01826019335709531]
  Sci-Orchestra is a layered orchestration framework designed to fully automate experimental operations.<n>By abstracting execution through an API-driven interface, the system assumes responsibility for secure authentication, resource management, and scalable deployment.<n>A key innovation of Sci-Orchestra is its autonomous marketplace, which serves as a catalyst for cross-institutional collaboration.
  arXiv  Detail & Related papers  (2026-05-08T19:03:40Z)
• Portable and Secure CI/CD for COBOL: Lessons from an Industrial Migration [0.6499706858965408]
  This paper presents an industrial case study of Bankdata, a cooperative IT provider for Danish banks, where a Jenkins-based CI/CD pipeline had grown fragile, slow and tightly coupled to platform-specific logic.<n>We report on the migration to a containerized architecture featuring an abstraction layer for platform logic, simplified repository structure, and a pre-built OCI-compliant image.<n>Our experience highlights lessons on abstraction, containerization, and organizational adoption, offering guidance for modernizing pipelines in legacy, high-security environments.
  arXiv  Detail & Related papers  (2026-04-01T14:12:04Z)
• REGAL: A Registry-Driven Architecture for Deterministic Grounding of Agentic AI in Enterprise Telemetry [0.0]
  Large Language Models (LLMs) enable new forms of agentic automation.<n>We present REGAL, a registry-driven architecture for deterministic grounding of agentic AI systems in enterprise telemetry.
  arXiv  Detail & Related papers  (2026-03-03T14:13:39Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• Autonomous Issue Resolver: Towards Zero-Touch Code Maintenance [0.0]
  We propose a paradigm shift from the standard Code Property Graphs to the concept of Data Transformation Graph (DTG)<n>We introduce a multi-agent framework that reconciles data integrity navigation with control flow logic.<n>Our approach has demonstrated good results on several SWE benchmarks, reaching a resolution rate of 87.1%.
  arXiv  Detail & Related papers  (2025-12-09T11:11:37Z)
• Everything is Context: Agentic File System Abstraction for Context Engineering [11.63011212134865]
  This paper proposes a file-system abstraction for context engineering.<n>The abstraction offers a persistent, governed infrastructure for managing heterogeneous context artefacts.<n>As GenAI becomes an active collaborator in decision support, humans play a central role as curators, verifiers, and co-reasoners.
  arXiv  Detail & Related papers  (2025-12-05T06:56:45Z)
• Automated Cloud Infrastructure-as-Code Reconciliation with AI Agents [8.4355888644149]
  NSync is an automated system for IaC reconciliation that propagates out-of-band changes back into the IaC program.<n>NSync gleans insights from API traces to detect drift (i.e., non-IaC changes) and reconcile it.
  arXiv  Detail & Related papers  (2025-10-23T04:57:00Z)
• The Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes [13.108419500875543]
  The EU's Artificial Intelligence Act introduces AI Regulatory Sandboxes (AIRS): supervised environments where AI systems can be tested under the oversight of Competent Authorities (CAs)<n>Yet significant challenges remain: assessment methods are fragmented, tests lack standardisation, and feedback loops between developers and regulators are weak.<n>We propose the Sandbox Configurator, a modular open-source framework that enables users to select domain-relevant tests from a shared library and generate customised sandbox environments with integrated dashboards.
  arXiv  Detail & Related papers  (2025-09-27T16:37:46Z)
• VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction [78.34534983766973]
  VehicleWorld is the first comprehensive environment for the automotive domain, featuring 30 modules, 250 APIs, and 680 properties.<n>We propose State-based Function Call (SFC), a novel approach that maintains explicit system state awareness and implements direct state transitions to achieve target conditions.
  arXiv  Detail & Related papers  (2025-09-08T14:28:25Z)
• Code2MCP: Transforming Code Repositories into MCP Services [53.234097255779744]
  Model Context Protocol (MCP) aims to create a standard for how Large Language Models use tools.<n>We introduce Code2MCP, an agent-based framework that automatically transforms a GitHub repository into a functional MCP service.<n>We demonstrate that Code2MCP successfully transforms open-source computing libraries in scientific fields such as bioinformatics, mathematics, and fluid dynamics.
  arXiv  Detail & Related papers  (2025-09-07T06:13:25Z)
• VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use [78.29315418819074]
  We introduce VerlTool, a unified and modular framework that addresses limitations through systematic design principles.<n>Our framework formalizes ARLT as multi-turn trajectories with multi-modal observation tokens (text/image/video), extending beyond single-turn RLVR paradigms.<n>The modular plugin architecture enables rapid tool integration requiring only lightweight Python definitions.
  arXiv  Detail & Related papers  (2025-09-01T01:45:18Z)
• Cloud Infrastructure Management in the Age of AI Agents [8.243598669679354]
  We make a case for developing AI agents powered by large language models (LLMs) to automate cloud infrastructure management tasks.<n>In a preliminary study, we investigate the potential for AI agents to use different cloud/user interfaces.<n>We report takeaways on their effectiveness on different management tasks, and identify research challenges and potential solutions.
  arXiv  Detail & Related papers  (2025-06-13T22:50:12Z)
• Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
  We investigate the security implications of and software-based Open Radio Access Network (RAN) systems. We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
  arXiv  Detail & Related papers  (2024-05-03T07:18:45Z)
• OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators [57.145175475579315]
  This topic spans various techniques, from structured pruning to neural architecture search, encompassing both pruning and erasing operators perspectives. We introduce the third-generation Only-Train-Once (OTOv3), which first automatically trains and compresses a general DNN through pruning and erasing operations. Our empirical results demonstrate the efficacy of OTOv3 across various benchmarks in structured pruning and neural architecture search.
  arXiv  Detail & Related papers  (2023-12-15T00:22:55Z)
• Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey [68.8204255655161]
  The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms.
  arXiv  Detail & Related papers  (2022-04-02T08:12:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.