AI-Driven Optimization Techniques for Dynamic Resource Allocation in Cloud Networks

Abstract

Dynamic resource allocation in cloud networks is essential for optimizing performance and reducing costs in modern distributed systems. Traditional static resource management methods often fail to adapt to fluctuating workloads, resulting in inefficiencies. AI-driven optimization techniques offer a solution by enabling real-time adaptability, predictive analysis, and intelligent decision-making. This paper explores various AI-based approaches, including machine learning (ML), reinforcement learning (RL), and deep learning (DL), that enhance dynamic resource allocation in cloud environments. It highlights the importance of these techniques in managing resource allocation based on traffic patterns, user demand, and system requirements. Furthermore, the challenges related to scalability, latency, and real-time processing are examined, along with potential future advancements. By employing AI-driven strategies, cloud networks can achieve superior load balancing, improved energy efficiency, and reduced operational costs, thereby revolutionizing cloud computing infrastructure.