Context-Aware AI Models for Dynamic Resource Management in Cloud Systems

Abstract

Cloud computing provides scalable and cost-effective resources for modern digital enterprises, but increasing workload diversity, changing user demands, and complex infrastructures make resource management challenging. Traditional resource allocation methods often fail to adapt to dynamic cloud environments, resulting in inefficient resource usage, SLA violations, and higher operational costs. This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives. The framework combines context acquisition, real-time analytics, Long Short-Term Memory (LSTM) workload prediction, Deep Reinforcement Learning (DRL)-based optimization, and adaptive orchestration. Experimental results show improved resource utilization, response time, energy efficiency, cost reduction, and service reliability. The framework supports autonomous cloud management and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.