Automating DevOps for Continuous Delivery and Operational Excellence

Authors:
Harikiran Boye, Srinivas Venkata

Addresses:
Department of DevOps, VISA, Research Blvd, Austin, Texas, United States of America. Department of Data Engineering, Teradata, Houston, Texas, United States of America. hboye@visa.com, srinivas.venkata@teradata.com

Abstract:

Integrating automation into DevOps practices has become crucial for achieving continuous delivery and operational excellence in software development. This research paper explores the methodologies and benefits of automating DevOps processes, focusing on how automation facilitates seamless collaboration, reduces errors, accelerates software delivery, and ensures consistent quality. The study discusses the implementation of automated pipelines, monitoring, and feedback mechanisms that enhance the efficiency of software deployment. Organizations can achieve faster release cycles, improve scalability, and maintain high availability by automating repetitive tasks and enabling real-time feedback loops. Various data sets were analyzed to support the findings, and tools such as Python, MATLAB, and MS Excel were utilized. Python was used for data analysis and visualization, particularly in generating graphs to illustrate trends in deployment frequency and failure rates. MATLAB was employed to create detailed architecture diagrams and mesh plots that depict the relationship between automation levels and deployment outcomes. MS Excel organized and presented numerical data in tables, comparing performance metrics in manual versus automated environments. Results from the study indicate that automation streamlines the software development lifecycle and fosters a culture of continuous improvement and innovation. The paper concludes by discussing the limitations of current automation technologies and suggesting future directions for enhancing DevOps automation.

Keywords: DevOps Automation; Continuous Delivery; Operational Excellence; Software Deployment; Pipeline Integration; Mean Time to Recovery (MTTR); Software Delivery Life Cycle.

Received on: 22/08/2023, Revised on: 17/11/2023, Accepted on: 24/01/2024, Published on: 01/03/2024

DOI: 10.69888/FTSCS.2024.000195

FMDB Transactions on Sustainable Computing Systems, 2024 Vol. 2 No. 1, Pages: 32-42

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