A Generative AI Framework for Self-Learning and Autonomous Optimization in Cloud Infrastructure Management
Abstract
The rapid evolution of cloud computing has intensified the need for intelligent, adaptive management systems capable of handling complex workloads, resource provisioning, and energy optimization autonomously. This review explores a Generative Artificial Intelligence (AI) Framework for Self-Learning and Autonomous Optimization in Cloud Infrastructure Management, emphasizing how generative models such as Generative Adversarial Networks , Variational Autoencoders , and Large Language Models enable proactive decision-making across distributed environments. The paper examines how generative AI can synthesize synthetic workload scenarios, predict optimal resource allocation, and generate dynamic optimization policies based on continuous feedback loops. It also investigates reinforcement learning integration to enhance fault tolerance, load balancing, and cost efficiency in multi-cloud ecosystems. Furthermore, the study addresses interpretability, scalability, and security concerns in deploying self-optimizing generative frameworks. By uniting predictive analytics, reinforcement signals, and generative modeling, the proposed framework aims to create a self-learning infrastructure capable of autonomous adaptation to evolving operational conditions. The review concludes with future research directions on explainable generative intelligence and trust calibration for autonomous cloud systems, establishing a pathway toward resilient, energy-aware, and cost-effective cloud infrastructure management in Industry 5.0.
Keywords
Generative AI; Cloud Infrastructure Management; Autonomous Optimization; Reinforcement Learning; Self-Learning Systems; Predictive Analytics.
References
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