Hybrid-learning-assisted impairments abstraction framework for service planning and provisioning over multi-domain optical networks

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 mach...

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Bibliographic Details
Published in:	Journal of optical communications and networking 2021-02, Vol.13 (2), p.A165-A177
Main Authors:	Wang, Rui, Nejabati, Reza, Simeonidou, Dimitra
Format:	Article
Language:	eng
Subjects:	Adaptive optics Degradation Domains Machine learning Monitoring Noise levels Optical communication Optical fiber networks Optical noise Optical receivers Performance evaluation Provisioning Signal to noise ratio
Online Access:	Get full text
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Summary:	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.
ISSN:	1943-0620 1943-0639