On Traffic Matrix Estimation via Super-Resolution and Federated Learning

Network traffic telemetry plays a crucial role in the management of modern networks. Estimation of the network traffic matrix is a widely recognized problem whose solutions can span a diverse set of applications. Current approaches to traffic matrix inference through statistical methods often rely on assumptions about the matrix structure, which may be invalid in certain scenarios. Data-driven methods, instead, often use detailed information about the network topology that may be unavailable or impractical to collect. To overcome these challenges, we propose a super-resolution technique for traffic matrix inference that leverages coarser measurements to predict fine-grained network traffic. Furthermore, we devise a distributed learning procedure and adapt our model to scenarios of partial network visibility. Our experiments on real network traces demonstrate that the proposed approach can infer fine-grained network traffic with high precision. Moreover, we prove that our distributed approach improves the inference accuracy with respect to its centralized counterpart, significantly lowering the training time, even in scenarios with partial network knowledge.