Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

This work presents supervised machine learning techniques for the problem of virtualization design with protection over elastic optical networks (EONs) for predicting the total number of used spectrum slots to support all traffic demands. It considers virtual optical networks (VONs) subject to prote...

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Bibliographic Details
Published in:Optical and quantum electronics 2024-05, Vol.56 (6), Article 1075
Main Authors: de Melo, Talison Augusto Correia, dos Santos, Alex Ferreira, de Andrade Almeida, Raul Camelo, Assis, Karcius Day Rosário
Format: Article
Language:English
Subjects:
Characterization and Evaluation of Materials
Computer Communication Networks
Electrical Engineering
Lasers
Optical Devices
Optics
Photonics
Physics
Physics and Astronomy
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Summary:This work presents supervised machine learning techniques for the problem of virtualization design with protection over elastic optical networks (EONs) for predicting the total number of used spectrum slots to support all traffic demands. It considers virtual optical networks (VONs) subject to protection and proposes learning techniques to solve the link capacity problem of EONs with virtualization faster than traditional integer linear programming (ILP) formulations, but keeping the finds near to the optimal ones. The performance of the models were evaluated using statistical metrics, along with the time for training and performing inferences, both using quantitative and qualitative analysis. They showed that the proposed method is effective for predicting the number of required slots (bandwidth) on physical substrate subject to several VONs.
ISSN:1572-817X
1572-817X
DOI:10.1007/s11082-024-07016-z