Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks

• Published in: Journal of lightwave technology Vol. 37; no. 1; pp. 218 - 225
• Main Authors: Liu, Gengchen, Zhang, Kaiqi, Chen, Xiaoliang, Lu, Hongbo, Guo, Jiannan, Yin, Jie, Proietti, Roberto, Zhu, Zuqing, Yoo, S. J. Ben
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognition; Engineering; Machine learning; modulations; Monitoring; Multi-domain networking; Neural networks; Operation support systems; Optical communication; Optical network units; optical networks; Optics; Provisioning; Q-factor; Telecommunications
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2018.2883898

This paper demonstrates, for the first time to our knowledge, hierarchical learning framework for inter-domain service provisioning in software-defined elastic optical networking (EON). By using a broker-based hierarchical architecture, the broker collaborates with the domain managers to realize efficient global service provisioning without violating the privacy constrains of each domain. In the proposed hierarchical learning scheme, machine learning-based cognition agents exist in the domain managers as well as in the broker. The proposed system is experimentally demonstrated on a two-domain seven-node EON testbed for with real-time optical performance monitors (OPMs). By using over 42000 datasets collected from OPM units, the cognition agents can be trained to accurately infer the Q -factor of an unestablished or established lightpath, enabling an impairment-aware end-to-end service provisioning with an prediction Q -factor deviation less than 0.6 dB.