@INPROCEEDINGS{Sacc2410:Inferring,
AUTHOR="Lorenzo Pappone and Cristian Zilli and Alessio Sacco and Flavio Esposito",
TITLE="Inferring {Fine-Grained} Traffic Matrices via Distributed Deep Residual
Networks",
BOOKTITLE="2024 20th International Conference on Network and Service Management (CNSM)
(CNSM 2024)",
ADDRESS="Prague, Czech Republic",
PAGES="8.86",
DAYS=27,
MONTH=oct,
YEAR=2024,
KEYWORDS="traffic prediction; federated learning; deep learning",
ABSTRACT="Network measurement and telemetry techniques are central to the management
of modern computer networks. Internet traffic matrix estimation is a
popular technique employed for network management and telemetry to
reconstruct missing information. Existing approaches use statistical
methods, which often make impractical assumptions about the structure of
the Internet traffic matrix. Data-driven methods, instead, heavily rely on
the assumption of full knowledge of network topology data, that may be
unavailable or impractical to collect.
In this work, we propose ResCue, a deep residual networks technique to
infer fine-grained Internet network traffic starting from spatial
coarse-grained measurements.  We further design a distributed learning
approach for fine-grained traffic prediction with partial network knowledge
to deal with network visibility constraints. Our evaluation across
real-world traffic data shows that our proposed approach outperforms
existing interpolation techniques and that our distributed learning design
achieves similar accuracy with respect to its centralized counterpart while
requiring only partial knowledge of the network."
}