As an important bearer network of the fifth generation (5G) mobile communication technology, the optical transport network (OTN) needs to have high-quality network performance and management capabilities. Proof by facts, the combination of artificial intelligence (AI) technology and software-defined networking (SDN) can improve significant optimization effects and management for optical transport networks. However, how to properly deploy AI in optical networks is still an open issue. The training process of AI models depends on a large amount of computing resources and training data, which undoubtedly increases the carrying burden and operating costs of the centralized network controller. With the continuous upgrading of functions and performance, small AI-based chips can be used in optical networks as on-board AI. The emergence of edge computing technology can effectively relieve the computation load of network controllers and provide high-quality AI-based networks optimization functions. In this paper, we describe an architecture called self-optimizing optical network (SOON) with cloud-edge collaboration, which introduces control-layer AI and on-board AI to achieve intelligent network management. In addition, this paper introduces several cloud-edge collaborative strategies and reviews some AI-based network optimization applications to improve the overall network performance.

中文翻译：

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云边协同自优化光网络：架构与应用


光传输网络（OTN）作为第五代（5G）移动通信技术的重要承载网络，需要具备高质量的网络性能和管理能力。事实证明，人工智能（AI）技术与软件定义网络（SDN）的结合可以显着提高光传送网络的优化效果和管理。然而，如何在光网络中正确部署人工智能仍然是一个悬而未决的问题。 AI模型的训练过程依赖于大量的计算资源和训练数据，这无疑增加了集中式网络控制器的承载负担和运营成本。随着功能和性能的不断升级，基于AI的小型芯片可以作为板载AI应用于光网络中。边缘计算技术的出现可以有效减轻网络控制器的计算负担，提供高质量的基于人工智能的网络优化功能。在本文中，我们描述了一种云边协同的自优化光网络（SOON）架构，引入控制层人工智能和板载人工智能来实现智能网络管理。此外，本文还介绍了几种云边协同策略，并回顾了一些基于人工智能的网络优化应用，以提高整体网络性能。