Abstract

This paper proposes and demonstrates a hybrid-learning-assisted impairments abstraction framework for planning and provisioning intra- and inter-domain services in a field-trial multi-domain optical network testbed. The proposed abstraction strategy consists of a parametric and a non-parametric machine learning technique to allow the control plane to implement impairments abstraction with different accessible data or monitoring technologies in the data plane. The hybrid-learning-assisted abstraction framework aims to abstract the property of segmental links along the lightpath and combine them for end-to-end performance evaluation. By deploying the proposed abstraction framework, network providers or operators are able to exchange the abstracted information for end-to-end impairments abstraction without revealing detailed information within each network. We experimentally demonstrate the proposed solution over a three-network field-trial testbed with real monitored data. The hybrid-learning-assisted impairments abstraction proves to be an accurate abstraction tool, with an average of 0.33 dB end-to-end signal-to-noise-ratio estimation error for services across the three networks.

© 2020 Optical Society of America

Decentralizing machine-learning-based QoT estimation for sliceable optical networks

Tania Panayiotou, Giannis Savva, Ioannis Tomkos, and Georgios Ellinas
J. Opt. Commun. Netw. 12(7) 146-162 (2020)

Machine learning-assisted nonlinear-impairment-aware proactive defragmentation for C+L band elastic optical networks

Rana Kumar Jana, Bijoy Chand Chatterjee, Abhishek Pratap Singh, Anand Srivastava, Biswanath Mukherjee, Andrew Lord, and Abhijit Mitra
J. Opt. Commun. Netw. 14(3) 56-68 (2022)

Hierarchical Distributed Orchestration Framework for Multi-Domain SDTNs

Jin Seek Choi
J. Opt. Commun. Netw. 9(12) 1125-1135 (2017)

Performance studies of evolutionary transfer learning for end-to-end QoT estimation in multi-domain optical networks [Invited]

Che-Yu Liu, Xiaoliang Chen, Roberto Proietti, and S. J. Ben Yoo
J. Opt. Commun. Netw. 13(4) B1-B11 (2021)

Demonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks

Xiaoliang Chen, Baojia Li, Roberto Proietti, Che-Yu Liu, Zuqing Zhu, and S. J. Ben Yoo
Opt. Express 27(24) 35700-35709 (2019)