AI/ML-Driven Cellular Monitoring in SD-WAN: Evolution and Implementation

Abstract

Cellular networks integration into Software-Defined Wide Area Networks (SD-WAN) has radically changed the paradigm of enterprise connectivity and has provided unprecedented flexibility and resiliency within the more distributed operational environments. The given scholarly article evaluates the evolutionary path of the monitoring approaches in this field and compares the traditional methods with the new artificial intelligence-based solutions. Conventional cellular monitoring of SD-WAN-based systems has traditionally been anchored on manual configurations, protocol-based polling processes, and reactive troubleshooting processes, which are not effective when facing the natural variability of wireless connections. Such traditional approaches are often limited by scalability constraints, latency sensitivities, and failure to proactively respond to performance degradations in cellular connections. On the contrary, newer-generation deployments take advantage of advanced AI and machine learning engines to provide predictive analytics features, behavioral anomaly detection engines, and autonomous optimization features, all of which collectively optimize network performance, security posture, and operational efficiency. Based on modern research and application examples, this article confirms that AI-based monitoring solutions will play a central role in future SD-WAN infrastructures, especially in latency-sensitive application environments like Internet of Things systems and edge computing systems. The article ends with extensive recommendations for the implementation of these transformational technologies in enterprise networking settings.