Open AccessDemonstration of distributed collaborative learning with end-to-end QoT estimation in multi-domain elastic optical networks
Author(s) -
Xiaoliang Chen,
Baojia Li,
Roberto Proietti,
Che-Yu Liu,
Zuqing Zhu,
Sung Jong Yoo
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.035700
Subject(s) - computer science , exploit , domain (mathematical analysis) , visibility , transmission (telecommunications) , provisioning , estimator , artificial intelligence , machine learning , computer network , optics , telecommunications , mathematical analysis , statistics , physics , computer security , mathematics
This paper proposes a distributed collaborative learning approach for cognitive and autonomous multi-domain elastic optical networking (EON). The proposed approach exploits a knowledge-defined networking framework which leverages a broker plane to coordinate the operations of multiple EON domains and applies machine learning (ML) to support autonomous and cognitive inter-domain service provisioning. By employing multiple distributed ML blocks learning domain-level features and working with broker plane aggregation ML blocks (through the chain rule-based training), the proposed approach enables to develop cognitive networking applications that can fully exploit the multi-domain EON states while obviating the need for the raw and confidential intra-domain data. In particular, we investigate end-to-end quality-of-transmission estimation application using the distributed learning approach and propose three estimator designs incorporating the concepts of multi-task learning (MTL) and transfer learning (TL). Evaluations with experimental data demonstrate that the proposed designs can achieve estimation accuracies very close to (with differences less than 0.5%) or even higher than (with MTL/TL) those of the baseline models assuming full domain visibility.