Machine learning (ML) techniques are being increasingly used in mobile networks for network planning, operation, management, optimisation and much more. These techniques are realised using a set of logical nodes known as ML pipeline. A single network operator might have thousands of such ML pipelines distributed across its network. These pipelines need to be managed and orchestrated across network domains. Thus it is essential to have autonomic multi-domain orchestration of ML pipelines in mobile networks. International Telecommunications Union (ITU) has provided an architectural framework for management and orchestration of ML pipelines in future networks. We extend this framework to enable autonomic orchestration of ML pipelines across multiple network domains. We present our system architecture and describe its application using a smart factory use case. Our work allows autonomic orchestration of multi-domain ML pipelines in a standardised, technology agnostic, privacy preserving fashion.

中文翻译：

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未来网络中机器学习管道的自主编排

机器学习 (ML) 技术越来越多地用于移动网络，用于网络规划、运营、管理、优化等。这些技术是使用一组称为 ML 管道的逻辑节点来实现的。单个网络运营商可能在其网络中分布着数千个此类 ML 管道。这些管道需要跨网络域进行管理和编排。因此，在移动网络中对 ML 管道进行自主的多域编排至关重要。国际电信联盟 (ITU) 为未来网络中 ML 管道的管理和编排提供了架构框架。我们扩展了这个框架，以实现跨多个网络域的 ML 管道的自主编排。我们展示了我们的系统架构，并使用智能工厂用例描述了其应用。我们的工作允许以标准化、技术不可知、隐私保护的方式自主协调多域机器学习管道。