Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks
Hierarchical Learning for Cognitive End-to-End Service Provisioning in Multi-Domain Autonomous Optical Networks
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DOI：
10.1109/jlt.2018.2883898
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发表时间：
2019-01
影响因子：
4.7
通讯作者：
Gengchen Liu;Kaiqi Zhang;Xiaoliang Chen;Hongbo Lu;J. Guo;Jie Yin;R. Proietti;Zuqing Zhu;
中科院分区：
文献类型：
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作者：
Gengchen Liu;Kaiqi Zhang;Xiaoliang Chen;Hongbo Lu;J. Guo;Jie Yin;R. Proietti;Zuqing Zhu;
本文展示了，第一次我们所知，分层学习框架域间服务提供的软件定义的弹性光网络（EON）。通过使用基于代理的分层体系结构，代理与域管理器协作以实现高效的全局服务提供，而不违反每个域的隐私约束。在所提出的分层学习方案中，基于机器学习的认知代理存在于域管理器和代理中。该系统在一个两域七节点的EON实验平台上进行了实验验证，并配备了实时光性能监测器（OPM）。通过使用从OPM单元收集的超过42000个数据集，可以训练认知代理以准确地推断未建立或已建立光路的Q因子，从而实现具有预测Q因子偏差小于0.6 dB的损伤感知端到端服务提供。
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.