AI-Driven Infrastructure Automation for Autonomous Cloud Operations and Fault Remediation (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
The rapid expansion of cloud computing has introduced unprecedented complexity in managing large- scale distributed infrastructure. Traditional infrastructure management approaches, which rely heavily on manual intervention and rule-based automation, struggle to keep pace with dynamic workloads, elastic resource allocation, and increasingly sophisticated application architectures. As enterprises migrate mission-critical systems to hybrid and multi-cloud environments, the need for intelligent, self-managing infrastructure has become essential. Artificial Intelligence (AI) has emerged as a transformative technology capable of enabling autonomous cloud operations by integrating predictive analytics, machine learning-driven anomaly detection, and automated remediation mechanisms directly into infrastructure management processes. AI-driven infrastructure automation represents a paradigm shift from reactive operational models toward proactive and self-healing cloud ecosystems. By leveraging machine learning algorithms, telemetry data, and continuous monitoring frameworks, AI systems can identify patterns in infrastructure behavior, predict potential failures, and automatically initiate corrective actions without human intervention. These capabilities significantly reduce operational downtime, improve system reliability, and enhance resource utilization across cloud platforms. Furthermore, autonomous remediation frameworks enable systems to dynamically adjust configurations, restart failed services, or reallocate workloads in response to detected anomalies. This paper explores the architectural foundations, operational mechanisms, and strategic benefits of AI-driven infrastructure automation in modern cloud environments. It examines how intelligent orchestration platforms integrate monitoring systems, predictive analytics engines, and automated remediation workflows to achieve autonomous infrastructure management. Additionally, the study discusses the role of AI in optimizing cloud performance, improving fault tolerance, and enabling continuous service availability in large-scale enterprise environments. The research also analyzes key implementation challenges such as data quality requirements, model interpretability, governance considerations, and integration with existing DevOps and Site Reliability Engineering (SRE) frameworks. Through conceptual architecture models and practical implementation strategies, the paper demonstrates how organizations can transition from conventional infrastructure management toward fully autonomous cloud operations. Ultimately, AI-driven automation represents a foundational step toward the development of self-optimizing digital infrastructure capable of supporting next-generation enterprise applications.
Keywords: AI-driven infrastructure automation; Autonomous cloud operations; Intelligent fault remediation; Cloud infrastructure management; Predictive infrastructure analytics; Self-healing systems; Machine learning in cloud operations; AIOps; Cloud reliability engineering; Automated incident response