Recursive Router Metrics Prediction Using ML-based Node Modeling for Network Digital Replica

Future network infrastructures will need to provide network services safely and rapidly under complex conditions that include accommodating many devices and multiple access lines such as 5G / 6G supported by multiple carriers. Further-more, future carrier networks will support network disaggre-gation technologies to leverage best-of-breed technology from different suppliers in accordance with the service requirements. Therefore, the efficiency of the verification needs to be improved for the combinations of a large amount of various network equipment and components constituting the network infrastructure to ensure network quality and reliability for unknown network conditions. The issue focused on this study is how to improve the prediction accuracy for the metrics of black-boxed network nodes when only the network node settings and traffic conditions are known as the external conditions. To address this, here, we propose machine learning based node modeling to improve the accuracy of predicted network node metrics by recursively adding other predicted metrics to the training datasets step by step in accordance with the feature importance. Experimental results show that the coefficient of determination (R2) of router metrics the throughput, the packet loss, and the packet delays, could be improved by using the training datasets including router settings and other predicted router metrics.