Network Digital Replica using Neural-Network-based Network Node Modeling

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. For this reason, the efficiency of the pre-verification needs to be improved for a large number of various devices to ensure safety and reliability. Furthermore, future carrier networks will support network disaggregation technologies to leverage best-of-breed technology from different suppliers in accordance with service requirements. Therefore, it is necessary to verify combinations of a large number of devices and the components constituting the network infrastructure to achieve optimal settings. In this paper, we propose the concept of network digital replica and a method of network node modeling to predict the performance of network nodes using neural-network-based machine learning. A network digital replica, which is a copy of a physical network, can be created in a digital domain not only to classify the specifications of network nodes but also to verify the performance for network devices digitally. We evaluate the effectiveness of the proposed method, which predicts the throughput and processing delays of actual routers on the basis of the sets of learning data including router settings and traffic conditions.