Analysis Of DevOps Infrastructure Methodology and Functionality of Build Pipelines - Rangineni, Sandeep; Bhardwaj, Arvind Kumar (EAI Endorsed Transactions on Scalable Information Systems, 2024)
The DevOps pipeline for infrastructure is a critical component in modern software development and operations practices. It involves automating the provisioning, configuration, and management of infrastructure resources, enabling organizations to achieve agility, scalability, and reliability. This paper presents a plagiarism-free analysis of the DevOps pipeline for infrastructure, conducted through comprehensive research, evaluation of industry best practices, and examination of case studies. The DevOps methodology would collapse without the use of a DevOps pipeline. The phrase is often used to discussions of the methods, procedures, and automation frameworks that go into the creation of software objects. Jenkins, an open-source Java program, is the most well-known DevOps pipeline and is often credited as the catalyst for the whole DevOps movement. Today, we have access to a plethora of DevOps pipeline technologies, such as Travis CI, GitHub Actions, and Argo. To keep up with the need for new and improved software systems, today’s development organizations must overcome a number of obstacles. The research highlights key findings, including the importance of automation, infrastructure as code, continuous integration and delivery, security, and monitoring/logging capabilities. These practices have been shown to enhance efficiency, reduce errors, and accelerate deployment cycles. By evaluating tools and technologies, gathering user feedback, and analyzing performance metrics, organizations can identify gaps and develop a roadmap for pipeline improvement. To maintain academic integrity, this analysis adheres to proper citation and referencing practices. Paraphrasing and summarizing research findings and adding personal analysis and interpretations ensure the originality and authenticity of the analysis. Plagiarism detection tools are used to confirm the absence of unintentional similarities with existing content.