AI-Assisted SD-WAN Link Selection to Prevent WAN Thrashing

The system performs preliminary actions by predicting future network performance using machine learning models before making link switching decisions. The ML model analyzes historical and current network metrics to forecast future stability, allowing the system to anticipate performance degradation and switch links proactively before actual instability occurs, thereby avoiding thrashing from reactive switching.

The system implements feedback mechanisms by continuously monitoring network performance metrics and feeding this information into machine learning models. The ML models learn from historical feedback patterns to predict future performance, enabling the system to make informed switching decisions only when predictions indicate sustained improvement, thereby reducing unnecessary switches and overhead while maintaining reliable response to actual performance degradation.

The system transforms the link selection approach by changing from static threshold-based parameters to dynamic machine learning model parameters. The ML models process multiple network metrics (latency, jitter, packet loss, bandwidth) and generate predictive parameters that dynamically adjust link selection criteria based on learned patterns, significantly improving link utilization efficiency while maintaining manageable complexity through automated model training and deployment.