An AI-Orchestrated Framework for Self-Learning Predictive Maintenance in Cloud Infrastructure

This research introduced a Deep Reinforcement Learning (DRL)-based Predictive Maintenance Framework for cloud infrastructure management, addressing the growing demand for adaptive, intelligent, and self-healing maintenance systems. Through the integration of telemetry-driven analytics, deep policy learning, and automated orchestration, the proposed system effectively transcends traditional static models by learning to anticipate, prevent, and mitigate faults in real time. Experimental results validated that the DRL model outperformed baseline algorithms such as LSTM, Random Forest, and Q-Learning across all major metrics, achieving superior fault prediction accuracy (96.3%), significant downtime reduction (42.1%), and notable cost savings (39.5%). The framework demonstrated robustness and scalability, maintaining consistent performance across increasing workloads and heterogeneous environments. The findings confirm that DRL’s adaptive learning capability provides a powerful mechanism for continuous optimization of cloud infrastructure. By autonomously balancing preventive and corrective maintenance, the system evolves into a self-optimizing ecosystem capable of responding to variable workloads and component degradation with minimal human intervention. The integration with orchestration platforms such as Kubernetes further enabled seamless execution of learned maintenance policies, enhancing system reliability and service continuity. Looking ahead, future research will explore multi-agent DRL architectures to coordinate maintenance across distributed and federated cloud environments, enabling collaborative fault management in multi-tenant systems. Additionally, incorporating federated learning and edge intelligence can extend predictive capabilities to geographically distributed data centers, ensuring privacy-preserving yet globally optimized maintenance strategies. Future enhancements may also involve explainable DRL (X-DRL) models to improve transparency and interpretability, addressing the challenge of black-box decision-making in critical infrastructure domains. Expanding the system into digital twin–based simulations will further allow real-time testing and validation of maintenance strategies before deployment.

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