Artificial Intelligence for Software-Defined Networking: A Comprehensive Review of Intelligent Control, Traffic Engineering, and Network Automation
Abstract
Software-Defined Networking (SDN) separates the control plane from the data plane, providing centralized programmability and flexibility for managing modern networks. However, the increasing scale and dynamism of SDN environments create challenges in traffic engineering, resource allocation, routing, fault management, and security. Artificial Intelligence (AI) provides opportunities to overcome these limitations by enabling data-driven and adaptive network control. This review comprehensively examines the application of machine learning, deep learning, reinforcement learning, and emerging generative AI techniques in SDN. Existing approaches for intelligent routing, congestion prediction, load balancing, controller placement, resource allocation, intrusion detection, and network configuration are systematically categorized and compared. Particular attention is given to the interaction between AI models and SDN controllers, including the requirements for real-time inference and continuous network adaptation. The review also discusses challenges related to training data, scalability, controller overhead, explainability, adversarial attacks, and model generalization. Finally, emerging research directions involving multi-agent reinforcement learning, graph neural networks, digital twins, and large language models are discussed for developing autonomous SDN infrastructures.
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