Estimation of traffic matrices via super-resolution and federated learning
Roberto Amoroso, Flavio Esposito, Maria Luisa Merani · 2020
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.