Streamlining Configuration Management: Accelerating Deployment and Reducing Development Effort in Scalable Systems

Sandeep Keshetti¹ & Dr. Saurabh Solanki²

¹University of Missouri-Kansas City

5000 Holmes St, Kansas City, MO 64110, United States

²Aviktechnosoft Private Limited

Govind Nagar, Mathura, UP, India, PIn-281001

Abstract– Configuration management (CM) is now an essential component of the scalable systems of today, particularly when organizations are seeking to accelerate deployment processes and reduce development effort. Evolution in DevOps practices, cloud-native environments, and automation tools has revolutionized configuration management in different ecosystems. However, with these advancements, there are still issues such as configuration drift, management of a single cloud, multiple clouds with compatibility, as well as manual intervention in complex environments. While Infrastructure as Code (IaC) and GitOps have significantly improved the automation of updating configurations, the need for predictive and real-time management solutions is largely in a state of incompleteness. This paper seeks to bridge the research gap that exists by exploring the recent advancements, such as the use of artificial intelligence and machine learning (AI/ML) for predictive configuration management, the role of blockchain technology in integrity and transparency, and the use of cloud management platforms for centralized configuration management. While various studies focus on the newest technologies for automating specific aspects of configuration management, few explore the integration of these new technologies into an end-to-end, holistic solution which can dynamically adapt to the evolving demands of scalable systems. Moreover, little research explores the possibility of service meshes and container orchestration in simplifying the hassle of configuration management in large-scale distributed systems. The research highlights the benefit of integrating AI-driven models, automation tools, and decentralized technologies to improve configuration management processes. This approach not only accelerates deployment but also increases system stability and scalability. Addressing the gaps in the current research body, this study brings new insights into how modern configuration management tools reduce the need for manual intervention, increase automation, and facilitate faster, more efficient deployment in scalable systems.

Keywords– Configuration management, DevOps, Infrastructure as Code, GitOps, automation, scalability, deployment acceleration, AI/ML, predictive configuration, configuration drift, service meshes, cloud-native platforms, container orchestration, blockchain, multi-cloud compatibility, continuous delivery, real-time configuration, automated testing, cloud management platforms, microservices architecture.

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