Authors: Tharushi Jayasuriya
Abstract: System downtime poses significant challenges for modern IT and industrial operations, often resulting in financial losses, productivity reductions, and compromised service quality. Traditional configuration management approaches, reliant on manual processes and static documentation, are prone to human error and delays, which can exacerbate system failures and prolong downtime. Intelligent configuration management systems (ICMS) have emerged as a transformative solution, leveraging artificial intelligence, machine learning, and predictive analytics to monitor, validate, and optimize system configurations in real time. These systems enable automated change tracking, anomaly detection, and proactive remediation, reducing the likelihood of misconfigurations and preventing system disruptions. By analyzing historical data and system dependencies, ICMS can predict potential failures and recommend corrective actions before incidents occur. This review examines the conceptual foundations, architectural frameworks, enabling technologies, and operational strategies that underpin intelligent configuration management. Additionally, it explores practical applications across IT infrastructure, cloud environments, manufacturing systems, and critical industrial operations, highlighting measurable reductions in downtime, improved reliability, and enhanced resource efficiency. The review also addresses challenges related to integration, data quality, security, and human oversight, while identifying future research directions such as autonomous self-healing systems, edge-enabled monitoring, and AI-enhanced root cause analysis. Intelligent configuration management is positioned as a strategic enabler for resilient, high-availability systems in complex operational environments.
DOI: http://doi.org/10.5281/zenodo.17883050
