Controller-Light CI/CD with Jenkins: Remote Container Builds and Automated Artifact Delivery

• URL: http://arxiv.org/abs/2511.05720v1
• Date: Fri, 07 Nov 2025 21:27:47 GMT
• Title: Controller-Light CI/CD with Jenkins: Remote Container Builds and Automated Artifact Delivery
• Abstract summary: This paper presents a controller-light CI/CD framework in which Jenkins runs as a containerized controller with persistent volumes.<n>Jenkins runs as a containerized controller with persistent volumes, delegating heavy build and packaging operations to a remote Docker host.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional Jenkins installations often perform resource-intensive builds directly on the controller, which can overload system resources and decrease reliability. This paper presents a controller-light CI/CD framework in which Jenkins runs as a containerized controller with persistent volumes, delegating heavy build and packaging operations to a remote Docker host. The controller container maintains secure SSH connections to remote compute nodes and focuses solely on orchestration and reporting. Atomic deployments with time-stamped backups, containerized build environments, immutable artifact packaging, and automatic notifications are all integrated into the system. Experimental evaluation shows reduced CPU and RAM usage on the controller, faster build throughput, and lower artifact delivery latency. For small and medium-sized DevOps organizations looking for scalable automation without adding orchestration complexity, this method offers a repeatable, low-maintenance CI/CD pipeline.

Related papers

• MCP-Universe RL: A Framework for Training MCP Tool-Use Agents via Reinforcement Learning [54.082979654934]
  Reinforcement learning (RL) has become an effective way to improve the tool-use ability of large language models (LLMs)<n>Most existing RL frameworks stop at the policy update.<n>We present MCP-Universe RL (MCP-U RL), an open-source framework that takes over both.
  arXiv  Detail & Related papers  (2026-08-23T01:49:04Z)
• Taming the Drift: Context-aware Repair of Dockerfile Drift during Software Evolution [52.50730821321986]
  Cadre is a context-aware framework for automated Dockerfile drift repair.<n>CDG maps each Dockerfile instruction to its file-level and inter-instruction dependencies.<n>DodeX mines real-world Dockerfile drift instances from GitHub Actions CI logs.
  arXiv  Detail & Related papers  (2026-07-14T09:12:26Z)
• GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines [22.29523291822444]
  We present GitInject, an open-source framework for evaluating prompt injection vulnerabilities in real, live GitHub.<n>We study workflow configurations across four AI providers and document eleven named attacks spanning config-file injection, credential exfiltration, judgment manipulation, and availability.<n>We find that all tested providers are susceptible to at least one attack class in their default configuration, and that the most critical vulnerabilities are structural.
  arXiv  Detail & Related papers  (2026-06-07T19:17:18Z)
• 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)
• Multi-Agent Computer Use [72.79887808312706]
  We argue that we should move towards evaluating and building multi-agent computer use (MACU) systems.<n>We propose a general multi-agent setup in which a manager model decomposes computer use tasks as a directed acyclic graph (DAG)<n>At each iteration, the manager dispatches parallel CUA subagents to carry out nodes on the ready frontier of the DAG.
  arXiv  Detail & Related papers  (2026-06-01T01:29:36Z)
• GitFarm: Git as a Service for Large-Scale Monorepos [0.0]
  We present GitFarm, a platform that provides Git as a stateful, identity-scoped, repository-centric execution service through a gRPC API.<n>GitFarm decouples repository management from clients by executing Git operations remotely within secure, ephemeral sandboxes backed by pre-warmed repositories.<n>Results demonstrate that Git as a service provides substantial performance and cost benefits, while preserving the flexibility of native Git semantics.
  arXiv  Detail & Related papers  (2026-04-13T19:09:04Z)
• RepoLaunch: Automating Build&Test Pipeline of Code Repositories on ANY Language and ANY Platform [49.43594274832262]
  We introduce RepoLaunch, the first agent capable of automatically resolving dependencies, compiling source code, and extracting test results for repositories across arbitrary programming languages and operating systems.<n>RepoLaunch automates the remaining steps, enabling scalable benchmarking and training of coding agents and LLMs.
  arXiv  Detail & Related papers  (2026-03-05T10:15:13Z)
• PPTAM$η$: Energy Aware CI/CD Pipeline for Container Based Applications [47.84270304529455]
  PPTAM$$ is an automated pipeline that integrates power and energy measurement into GitLab CI for containerised API systems.<n>The pipeline makes energy visible to developers, supports version comparison for test engineers and enables trend analysis for researchers.
  arXiv  Detail & Related papers  (2026-02-12T15:38:35Z)
• Learning to Share: Selective Memory for Efficient Parallel Agentic Systems [49.78267008828593]
  Agentic systems solve complex tasks by coordinating multiple agents that iteratively reason, invoke tools, and exchange intermediate results.<n>Recent approaches deploy multiple agent teams running in parallel to explore diverse reasoning trajectories.<n>We propose Learning to Share (LTS), a learned shared-memory mechanism for parallel agentic frameworks.
  arXiv  Detail & Related papers  (2026-02-05T18:20:21Z)
• SWE-World: Building Software Engineering Agents in Docker-Free Environments [91.17484806743641]
  SWE-World is a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents.<n>We show that SWE-World raises Qwen2.5-Coder-32B from 6.2% to 52.0% via Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with further TTS.
  arXiv  Detail & Related papers  (2026-02-03T11:44:39Z)
• Orchestral AI: A Framework for Agent Orchestration [45.946776875141666]
  Orchestral is a lightweight Python framework that provides a unified, type-safe interface for building LLM agents across major providers.<n>It operates seamlessly across providers, eliminating manual format translation and reducing framework-induced complexity.<n>It supports advanced agent capabilities found in larger frameworks, including rich tool calling, context compaction, sandboxing, user approval, sub-agents, memory management, and MCP integration.
  arXiv  Detail & Related papers  (2026-01-05T22:02:11Z)
• A General Solution for the Implementation of CI/CD in Embedded Linux Development [0.6445605125467574]
  This research was conducted with the aim of designing and implementing an integrated and reproducible infrastructure for the development, building, and testing of a Linux-based operating system using the Yocto Project.<n>The proposed structure was implemented based on a three-layer architecture consisting of the main Yocto repositories, a custom layer (meta-custom), and a coordinating manifest layer.<n> Continuous Integration and Continuous Deployment pipelines were implemented with GitLab CI and combined with an isolated Docker environment to automate and streamline the build and testing.
  arXiv  Detail & Related papers  (2025-10-22T04:48:41Z)
• PIPer: On-Device Environment Setup via Online Reinforcement Learning [74.52354321028493]
  Automated environment setup methods could assist developers by providing fully configured environments for arbitrary repositories without manual effort.<n>Recent studies reveal that even state-of-the-art Large Language Models (LLMs) achieve limited success in automating this task.<n>We combine supervised fine-tuning for generating correct scripts and Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task of environment setup.<n>On EnvBench-Python, our method enables Qwen3-8B (a model runnable on consumer hardware) to perform on par with larger models-Qwen3-32B and GPT-4
  arXiv  Detail & Related papers  (2025-09-29T20:03:05Z)
• 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)
• Git Context Controller: Manage the Context of LLM-based Agents like Git [6.521644491529639]
  Large language model (LLM) based agents have shown impressive capabilities by interleaving internal reasoning with external tool use.<n>We introduce Git-Context-Controller (GCC), a structured context management framework inspired by software version control systems.<n>In a self-replication case study, a GCC-augmented agent builds a new CLI agent from scratch, achieving 40.7 task resolution, compared to only 11.7 without GCC.
  arXiv  Detail & Related papers  (2025-07-30T08:01:45Z)
• EnvBench: A Benchmark for Automated Environment Setup [76.02998475135824]
  Large Language Models have enabled researchers to focus on practical repository-level tasks in software engineering domain.<n>Existing studies on environment setup introduce innovative agentic strategies, but their evaluation is often based on small datasets.<n>To address this gap, we introduce a comprehensive environment setup benchmark EnvBench.
  arXiv  Detail & Related papers  (2025-03-18T17:19:12Z)
• PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC [98.82146219495792]
  In this paper, we propose a hierarchical agent framework named PC-Agent.<n>From the perception perspective, we devise an Active Perception Module (APM) to overcome the inadequate abilities of current MLLMs in perceiving screenshot content.<n>From the decision-making perspective, to handle complex user instructions and interdependent subtasks more effectively, we propose a hierarchical multi-agent collaboration architecture.
  arXiv  Detail & Related papers  (2025-02-20T05:41:55Z)
• Repo2Run: Automated Building Executable Environment for Code Repository at Scale [10.143091612327602]
  We introduce Repo2Run, an agent aiming at automating the building of executable test environments for any repositories at scale.<n>Repo2Run iteratively builds the Docker image, runs unit tests based on the feedback of the building, and synthesizes the Dockerfile.<n>The resulting Dockerfile can then be used to create Docker container environments for running code and tests.
  arXiv  Detail & Related papers  (2025-02-19T12:51:35Z)
• Design and Implementation of Flutter based Multi-platform Docker Controller App [1.1443262816483672]
  This paper focuses on developing a Flutter application for controlling Docker resources remotely.<n>The application uses the SSH protocol to establish a secure connection with the server and execute the commands.<n>An alternative approach is also explored, which involves connecting the application with the Docker engine using HTTP.
  arXiv  Detail & Related papers  (2025-02-17T11:48:02Z)
• Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control [37.10638636086814]
  We consider a joint uplink and downlink scheduling problem of a fully distributed wireless control system (WNCS) with a limited number of frequency channels.<n>We develop a deep reinforcement learning (DRL) based framework for solving it.<n>To tackle the challenges of a large action space in DRL, we propose novel action space reduction and action embedding methods.
  arXiv  Detail & Related papers  (2021-09-26T11:27:12Z)
• Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion [95.1825179206694]
  We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
  arXiv  Detail & Related papers  (2020-09-21T16:49:26Z)
• dm_control: Software and Tasks for Continuous Control [24.588523489116326]
  The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The Control Suite is a fixed set of tasks with standardised structure, intended to serve as performance benchmarks.
  arXiv  Detail & Related papers  (2020-06-22T17:52:00Z)

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.