%0 Conference Proceedings
%T Machine Learning Assisted Quality of Transmission Estimation and Planning with Reduced Margins
%+ Nokia Bell Labs [Stuttgart]
%+ National Technical University of Athens [Athens] (NTUA)
%+ School of Electrical and Computer Engineering
%A Christodoulopoulos, Konstantinos
%A Sartzetakis, Ippokratis
%A Soumplis, Polizois
%A Varvarigos, Emmanouel
%Z Part 1: Regular Papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 23th International IFIP Conference on Optical Network Design and Modeling (ONDM)
%C Athens, Greece
%Y Anna Tzanakaki
%Y Manos Varvarigos
%Y Raul Muñoz
%Y Reza Nejabati
%Y Noboru Yoshikane
%Y Markos Anastasopoulos
%Y Johann Marquez-Barja
%I Springer International Publishing
%3 Optical Network Design and Modeling
%V LNCS-11616
%P 211-222
%8 2019-05-13
%D 2019
%R 10.1007/978-3-030-38085-4_19
%K Overprovisioning
%K Static network planning
%K End-of-life margins
%K Physical layer impairments
%K Monitoring
%K Cross-layer optimization
%K Incremental multi-period planning
%K Marginless
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X In optical transport networks, the Quality of Transmission (QoT) using a physical layer model (PLM) is estimated before establishing new or reconfiguring established optical connections. Traditionally, high margins are added to account for the model’s inaccuracy and the uncertainty in the current and evolving physical layer conditions, targeting uninterrupted operation for several years, until the end-of-life (EOL). Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) assisted QoT estimators that leverage monitoring data of existing connections to understand the actual physical layer conditions and achieve high estimation accuracy. We then quantify the benefits of planning/upgrading a network over multiple periods with accurate QoT estimation as opposed to planning with EOL margins.
%G English
%Z TC 6
%Z WG 6.10
%2 https://inria.hal.science/hal-03200661/document
%2 https://inria.hal.science/hal-03200661/file/484327_1_En_19_Chapter.pdf
%L hal-03200661
%U https://inria.hal.science/hal-03200661
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-LNCS-11616
%~ IFIP-ONDM
%~ IFIP-WG6-10