AI-Based Optimization Of Cloud Resource Allocation

Authors: Raghunath Mashelkar

Abstract: AI-based optimization of cloud resource allocation has emerged as a critical approach for improving the efficiency, scalability, and cost-effectiveness of modern cloud computing environments. As cloud systems support increasingly complex and dynamic workloads, traditional static and rule-based resource allocation methods often fail to adapt to fluctuating demand patterns. Artificial Intelligence (AI), particularly machine learning and reinforcement learning techniques, enables intelligent and adaptive allocation of computing resources such as CPU, memory, storage, and network bandwidth. This study explores how AI-driven models can predict workload demands, optimize resource provisioning, and enhance load balancing across distributed cloud infrastructures. It also examines key techniques such as predictive analytics, scheduling optimization, and autonomous resource management. Furthermore, the paper discusses integration with cloud orchestration platforms and highlights challenges such as data variability, model accuracy, latency constraints, and energy efficiency. Emerging solutions such as deep reinforcement learning, edge-cloud collaboration, and AIOps are also analyzed. The findings indicate that AI-based resource allocation significantly improves system performance, reduces operational costs, and ensures better utilization of cloud resources.

DOI: http://doi.org/