Network Tomography and Partial Least Squares for Traffic Matrix Estimation Cuberos, Francisco J. Herrera, Irene Wasielewska, Katarzyna Camacho Páez, José traffic matrix network tomography link counts partial least squares Abilene data set 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. 2023-04-24T07:42:09Z 2023-04-24T07:42:09Z 2021 conference output https://hdl.handle.net/10481/81202 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ open access Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License 17th International Conference on Network and Service Management (CNSM 2021)