Network measurement and telemetry techniques are central to the management of modern computer networks. Traffic matrices estimation is a popular technique that supports several applications. Existing approaches use statistical methods which often make invalid assumptions about the structure of the traffic matrix. Data-driven methods, instead, leverage detailed information about the network topology that may be unavailable or impractical to collect. In this work, we propose a super-resolution technique for traffic matrix estimation that can infer fine-grained network traffic. In our experiment, we demonstrate that the proposed approach with high precision outperforms existing data interpolation techniques. We also expand our design by employing a federated learning model to address scalability and improve performance. Such a model increases the accuracy of our inference with respect to its centralized counterpart, significantly lowering the number of training epochs.
This work is the result of a research project developed in collaboration with the Networking Research Group, led by Prof. Flavio Esposito, at the Saint Louis University of Saint Louis, in the United States.