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.

中文翻译：

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混合学习辅助减损抽象框架，用于在多域光网络上进行服务规划和配置

本文提出并演示了一种混合学习辅助减损抽象框架，用于在现场试用的多域光网络测试平台中规划和提供域内和域间服务。所提出的抽象策略包括参数化和非参数化机器学习技术，以允许控制平面使用数据平面中的不同可访问数据或监视技术来实现损伤抽象。混合学习辅助抽象框架旨在对沿光路的分段链接的属性进行抽象，并将其组合起来以进行端到端性能评估。通过部署建议的抽象框架，网络提供商或运营商能够交换抽象信息以进行端到端减损抽象，而无需透露每个网络内的详细信息。我们在具有实际监控数据的三网络现场试验试验台上实验性地证明了所提出的解决方案。混合学习辅助的减损抽象被证明是一种准确的抽象工具，三个网络中服务的端到端信噪比估计误差平均为0.33 dB。