Authors: Siti Rahmawati
Abstract: The rapid proliferation of cloud-native applications, hybrid work models, and bandwidth-intensive services has fundamentally challenged the static nature of traditional Wide Area Networks (WAN). Software-Defined WAN (SD-WAN) introduced a centralized control plane to decouple network software from hardware, yet the manual definition of steering policies often fails to account for the highly volatile nature of internet transport circuits. This review examines the paradigm shift toward Intelligent SD-WAN Management powered by Artificial Intelligence (AI) and Machine Learning (ML). By leveraging deep learning architectures and reinforcement learning agents, SD-WAN controllers can now transition from reactive, threshold-based switching to proactive, intent-driven optimization. This article explores the core methodologies of AI-integrated management, focusing on predictive traffic engineering, automated root cause analysis, and self-healing infrastructure. We analyze how AI models optimize Quality of Experience (QoE) for mission-critical applications—such as VoIP and real-time video—by analyzing multi-dimensional telemetry including jitter, latency, and packet loss in real-time. Furthermore, the review addresses the critical challenges of model interpretability in network operations, the "cold start" problem in new deployments, and the necessity for federated learning to ensure data privacy across multi-tenant SD-WAN environments. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building "Self-Driving WANs." The findings suggest that AI-integrated management not only reduces operational expenditure by automating complex routing decisions but also provides the cognitive intelligence required to manage the unpredictable performance of commodity internet underlays in a global digital economy.
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