Machine Learning-Based Dynamic Resource Allocation for Intelligent Cloud Systems
Cloud computing has emerged as the backbone of modern digital ecosystems, providing scalability, elasticity, and cost efficiency. However, static resource allocation strategies often collapse under fluctuating workloads, resulting in inefficiency, energy waste, and degraded performance. To address these challenges, this study proposes a machine learning (ML)-based dynamic resource allocation framework that enhances efficiency, adaptability, and resilience in intelligent cloud systems. Keywords: Cloud computing, resource allocation, machine learning, reinforcement learning, intelligent systems.