Scalable Architecture for Cloud-native Transport SDN Controller Using GNPy and Machine Learning techniques for QoT estimation

Name: Scalable Architecture for Cloud-native Transport SDN Controller Using GNPy and Machine Learning techniques for QoT estimation
Keywords: NO KEYWORDS

Manso, Carlos; Vilalta, Ricard; Muñoz, Raül; Casellas, Ramon; Martínez, Ricardo

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Conference object . 2021

License: CC BY

Data sources: Datacite

ZENODO

Conference object . 2021

License: CC BY

Data sources: Datacite

https://doi.org/10.5281/zenodo...

Article . 2021

License: CC BY

Data sources: Sygma

http://dx.doi.org/10.5281/zeno...

Conference object . 2021

Data sources: European Union Open Data Portal

Scalable Architecture for Cloud-native Transport SDN Controller Using GNPy and Machine Learning techniques for QoT estimation

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Jan 2021 English Publisher:ZenodoJournal:2021 Optical Fiber Communications Conference and Exhibition (OFC)Funded by:EC | TeraFlow

Authors: Manso, Carlos; Vilalta, Ricard; Muñoz, Raül; Casellas, Ramon; Martínez, Ricardo;

doi: 10.5281/zenodo.5087383 , 10.5281/zenodo.5087382

Scalable Architecture for Cloud-native Transport SDN Controller Using GNPy and Machine Learning techniques for QoT estimation

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Abstract

This demo shows a cloud-native SDN controller that can estimate end-to-end QoT using both analytical (GNPy) and machine learning algorithms on WDM systems. This transport SDN controller is able to scale horizontally using custom metrics. © 2021 The Author(s)

Grant numbers : AURORAS - Autonomic and disaggregated optical networks leveraging edge computing and photonic technologies (RTI2018-099178-B-I00) project.

Related Organizations

Centre Tecnologic De Telecomunicacions De Catalunya
Spain

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