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

• Published in: Optical and quantum electronics Vol. 56; no. 6
• Main Authors: de Melo, Talison Augusto Correia, dos Santos, Alex Ferreira, de Andrade Almeida, Raul Camelo, Assis, Karcius Day Rosário
• Format: Journal Article
• Language: English
• Published: New York Springer US 13.05.2024
• Subjects: Characterization and Evaluation of Materials; Computer Communication Networks; Electrical Engineering; Lasers; Optical Devices; Optics; Photonics; Physics; Physics and Astronomy; Elastic optical networks; Protection; Virtualization; Optimization; Machine learning
• ISSN: 1572-817X; 1572-817X
• DOI: 10.1007/s11082-024-07016-z

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.