Abstract

Considering flexible technologies available nowadays, operating optical networks much closer to their physical capacities is very tempting but necessarily requires efficient network automation. To achieve this, the two main challenges are handling failures, and accurately predicting performance in dynamic environments. We experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to preventively mitigate their impact, and thus guarantee smooth network operation. Then, leveraging machine learning for live performance estimation and closed-loop software-defined network control, we demonstrate a fully automated reconfiguration of marginless connections undergoing critical performance variations over 228 km of field-deployed fiber.

© 2018 IEEE

Toward efficient, reliable, and autonomous optical networks: the ORCHESTRA solution [Invited]

K. Christodoulopoulos, C. Delezoide, N. Sambo, A. Kretsis, I. Sartzetakis, A. Sgambelluri, N. Argyris, G. Kanakis, P. Giardina, G. Bernini, D. Roccato, A. Percelsi, R. Morro, H. Avramopoulos, P. Castoldi, P. Layec, and S. Bigo
J. Opt. Commun. Netw. 11(9) C10-C24 (2019)

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Experimental Validation of Time-Synchronized Operations for Software-Defined Elastic Optical Networks

Anderson Bravalheri, Miquel Garrich Alabarce, Abubakar Siddique Muqaddas, Paolo Giaccone, and Andrea Bianco
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Deep neural network based OSNR and availability predictions for multicast light-trees in optical WDM networks

Xin Li, Lu Zhang, Jianghua Wei, and Shanguo Huang
Opt. Express 28(7) 10648-10669 (2020)