The Role of AI and Machine Learning in Enhancing SD-WAN Performance

Abstract

Enterprise networks have grown more complex and cloud-based applications have become rampant to the extent that the traditional wide area network (WAN) architectures have been particularly strained. The problem of performance optimization, instant decision-making, and dynamic traffic management has continued to exist despite the introduction of Software-Defined Wide Area Networks (SD-WAN) as a flexible mix. In this paper, we presented the nature of partnership between Artificial Intelligence (AI) and Machine Learning (ML) techniques and SD-WAN architectures in order to assist in overcoming these limitations. Particularly, it focuses on the potential of supervised and reinforcement learning model to improve the task of traffic routing, forecast and neutralize network anomalies, and automated policy statements in accordance with the current network state. Using comparative analysis to counter AI-enhanced SD-WAN systems and traditional rule-based systems, the study shows improvement in several important performance measures that include latency, jitter, packet misplaced, and service level agreement (SLA) adherence. There is also a section in the paper that deals with the implementation challenges such as data privacy, model shift, and non-standardization of vendor platforms. The results paint the disruptive possibilities of AI and ML in achieving smarter, flexible, and robust SD-WAN infrastructure that can support the changing enterprise requirements.