Estimation of traffic matrices via super-resolution and federated learning.

Network measurement and telemetry techniques are central to the management of today's computer networks. One popular technique with several applications is the estimation of traffic matrices. Existing traffic matrix inference approaches that use statistical methods, often make assumptions on the structure of the matrix that may be invalid. Data-driven methods, instead, often use detailed information about the network topology that may be unavailable or impractical to collect.Inspired by the field of image processing, we propose a superresolution technique for traffic matrix inference that does not require any knowledge on the structural properties of the matrix elements to infer, nor a large data collection. Our experiments with anonymized Internet traces demonstrate that the proposed approach can infer fine-grained network traffic with high precision outperforming existing data interpolation techniques, such as bicubic interpolation.