Cloud computing environments have become the backbone of modern digital infrastructure, supporting large-scale distributed applications, real-time services, and mission-critical operations. However, the inherent complexity, scalability demands, and dynamic resource allocation introduce significant challenges in maintaining system reliability and fault tolerance. Traditional fault management approaches, which rely on rule-based or reactive mechanisms, are increasingly insufficient in handling the scale and unpredictability of contemporary cloud systems. This research proposes an autonomous fault management framework leveraging deep learning-based decision-making techniques, particularly deep reinforcement learning (DRL), to enable proactive, adaptive, and intelligent fault detection, diagnosis, and recovery.

The study integrates concepts from reinforcement learning, knowledge distillation, and federated learning to construct a scalable and efficient fault management architecture. By employing DRL models capable of learning optimal policies under uncertain and partially observable environments, the framework enhances decision-making in dynamic cloud infrastructures. Additionally, knowledge distillation techniques are incorporated to reduce model complexity while preserving performance, enabling deployment in resource-constrained environments. The proposed approach also explores distributed learning paradigms to address privacy and scalability concerns.

Through analytical modeling and simulated experimentation, the research demonstrates improved fault detection accuracy, reduced recovery time, and enhanced system resilience compared to traditional approaches. The findings indicate that deep learning-based autonomous systems can significantly transform cloud reliability engineering by enabling predictive maintenance and self-healing capabilities. However, challenges such as model interpretability, training overhead, and data dependency remain critical considerations.

This work contributes to the advancement of intelligent cloud management systems by providing a comprehensive framework that integrates multiple deep learning paradigms. It offers insights into the practical implementation of autonomous fault management and highlights future research directions, including hybrid learning models and real-time adaptive systems.