Intelligent resource orchestration for large-scale cloud environments using hybrid AI models

The importance of cloud computing lies in its provision as a critical infrastructure for large-scale applications and emerging technologies. Modern cloud computing infrastructures demand efficient resource orchestration for dynamic workloads and service-level agreement requirements. In this regard, traditional scheduling techniques are not efficient. This study proposes a novel intelligent resource orchestration framework based on hybrid artificial intelligence techniques that combine deep learning, reinforcement learning, and optimization algorithms for efficient resource management. The proposed architecture has different layers for analytics, decision engines, and orchestration functions that enable efficient resource management. Experimental results confirm that the proposed framework improves resource usage efficiency, task completion time, and operational cost efficiency for large-scale cloud computing infrastructures. The hybrid AI-based orchestration technique is efficient for scalability and adaptability for future cloud computing.