@misc{10481/81202,
year = {2021},
url = {https://hdl.handle.net/10481/81202},
abstract = {The traffic matrix is a useful data structure in network
management, monitoring, optimization and traffic forecast.
A recurrent problem is to obtain accurate traffic matrices in real
time from the traffic of a network, specially when this network
is large (e.g., a Tier 1 Internet Service Provider), and without
causing a relevant overhead in network computing, storage and
communication resources. A solution deeply investigated in the
past is the network tomography: the estimation of a traffic
matrix from the volume of traffic traversing the links (a.k.a.
link counts), which measurement implies a minimum overhead.
This estimation entails relevant challenges. In this paper, we
propose the application of the Partial Least Squares method to
this problem. We illustrate the proposal with the Abilene network
dataset, and report promising results in comparison to traditional
methods like General Tomogravity and the Structural Analysis
based on Principal Component Analysis.},
organization = {This work was supported by the Agencia Estatal de Investigaci´on in Spain,
grant No PID2020-113462RB-I00, and the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 893146.},
publisher = {17th International Conference on Network and Service Management (CNSM 2021)},
keywords = {traffic matrix},
keywords = {network tomography},
keywords = {link counts},
keywords = {partial least squares},
keywords = {Abilene data set},
title = {Network Tomography and Partial Least Squares for Traffic Matrix Estimation},
author = {Cuberos, Francisco J. and Herrera, Irene and Wasielewska, Katarzyna and Camacho Páez, José},
}