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dc.contributor.authorCuberos, Francisco J.
dc.contributor.authorHerrera, Irene
dc.contributor.authorWasielewska, Katarzyna
dc.contributor.authorCamacho Páez, José 
dc.date.accessioned2023-04-24T07:42:09Z
dc.date.available2023-04-24T07:42:09Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/10481/81202
dc.description.abstractThe 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.es_ES
dc.description.sponsorshipThis 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.es_ES
dc.language.isoenges_ES
dc.publisher17th International Conference on Network and Service Management (CNSM 2021)es_ES
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 3.0 Licenseen_EN
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en_EN
dc.subjecttraffic matrixes_ES
dc.subjectnetwork tomographyes_ES
dc.subjectlink countses_ES
dc.subjectpartial least squareses_ES
dc.subjectAbilene data setes_ES
dc.titleNetwork Tomography and Partial Least Squares for Traffic Matrix Estimationes_ES
dc.typeconference outputes_ES
dc.rights.accessRightsopen accesses_ES
dc.type.hasVersionSMURes_ES


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