Neural Processes-Based Node Modeling to Extrapolate Router Metrics

Future network infrastructures will become more complex, which will require fast and secure service delivery in unpredictable scenarios, including diverse devices and multiple 5G/6G access lines supported by different carriers. In addition, future carrier networks are expected to adopt network disaggregation technologies that integrate superior technologies from different vendors, which are often "black-boxed", to meet specific service requirements. We define a "black-boxed" node as a network node where the internal implementation of packet processing mechanisms is not disclosed, although hardware specifications are known, as seen in vendor products. This poses a challenge in the performance verification of network nodes and components for black-boxed network nodes. Consequently, a research issue emerges: the need to highly accurately estimate the performance of black-boxed network nodes in advance, where it is difficult to estimate the per-packet cost of how much bandwidth and computation time for a single packet consumes in the face of unexperienced scenarios. Therefore, the objective of this research is to explore the potential for digitally verifying the performance of black-boxed network nodes, focusing on refining the accuracy of extrapolation for their metrics. This extrapolation utilizes available external factors, including measured target metrics, node settings, and traffic conditions. In response, we propose a node modeling method that is a combination of neural processes, a type of meta-learner. The novelty of the proposed algorithm lies in its approach to iteratively append inferred router metrics to the training datasets based on feature importance. Experimental results demonstrate that by including router settings and inferred other router metrics in the training dataset based on software routers, the coefficient of determination for inferred router metrics; packet loss rates, throughput, and packet delays in the extrapolation domain surpasses the results obtained from the original training dataset alone.