AI-Driven Energy Optimization in SDN-Based Cloud Computing for Balancing Cost, Energy Efficiency, and Network Performance

Abstract

The rapid expansion of cloud computing has resulted in increasing energy demands, presenting a significant challenge to the sustainability of large-scale cloud infrastructures. Software-Defined Networking (SDN) improves flexibility, programmability, and central control for managing cloud networks, but energy consumption remains a persistent issue due to the large-scale processing of data and the continuous operation of networking devices. To address these challenges, Artificial Intelligence (AI) offers advanced methods for optimizing energy usage by providing real-time control and predictive analytics. This paper examines the development of AI-driven models for energy optimization in SDN-based cloud computing environments, focusing on machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques. AI models dynamically adjust cloud resources, predict network traffic patterns, and balance energy consumption against performance and cost constraints. The study explores AI architectures, their integration with SDN controllers, and methods to address the inherent trade-offs between energy efficiency, cost, and network performance. This study propose frameworks for AI-driven energy-aware management of SDN-enabled cloud environments and analyze the technical challenges of deploying scalable and adaptive solutions. The findings of this study indicate that AI-based optimization strategies can significantly reduce energy consumption in SDN-based cloud environments while maintaining high service levels, offering a path toward more efficient, cost-effective, and environmentally sustainable cloud infrastructures.