AI-Driven Optimization Techniques for Cloud Resource Management

Abstract

AI-driven optimization techniques have become pivotal in managing cloud resources amidst rapidly evolving workload demands and cost pressures. These techniques harness machine learning (ML), deep learning (DL), and reinforcement learning (RL) to enable predictive and dynamic resource allocation, reducing over-provisioning and enhancing service quality. This study synthesizes recent advances in AI-driven frameworks, including deep neural networks and model order reduction techniques, as well as autonomous, holistic scheduling systems, to provide a comprehensive perspective on optimizing resource utilization, reducing energy consumption, and improving system responsiveness. For example, a deep learning-based framework employing LSTM for demand forecasting paired with DQN for scheduling achieved a 32.5% improvement in resource utilization, a 43.3% reduction in response time, and a 26.6% reduction in operational costs in production cloud settings arXiv. Another hybrid approach integrating AI with model order reduction and advanced queueing models demonstrated a 50% reduction in response time, 50% increase in throughput, and 15% better resource utilization, alongside significant gains in energy efficiency and uptime reliability SpringerLink. Additionally, AI-based holistic scheduling systems targeting sustainability—such as the Gated Graph Convolutional Network-driven model—achieved up to 12% reductions in energy usage and 35% fewer SLA violations arXiv. We also review comparative analyses showing advantages of AI over traditional static allocation methods, highlighting gains in adaptability, cost-efficiency, and response performance Nucleus Corp Vectoral. The abstract summarizes emerging trends: predictive autoscaling, energy-aware scheduling, multi-objective optimizations, and hybrid RL–supervised models as future pillars of cloud optimization. References cover frameworks, comparative studies, and implementation considerations, offering both theoretical and practical insights for cloud service providers, infrastructure architects, and AI researchers.