Network slicing is a crucial enabler and a trend for the Next Generation Mobile Network (NGMN) and various other new systems like the Internet of Vehicles (IoV) and Industrial IoT (IIoT). Orchestration and machine learning are key elements with a crucial role in the network-slicing processes since the NS process needs to orchestrate resources and functionalities, and machine learning can potentially optimize the orchestration process. However, existing network-slicing architectures lack the ability to define intelligent approaches to orchestrate features and resources in the slicing process. This paper discusses machine learning-based orchestration of features and capabilities in network slicing architectures. Initially, the slice resource orchestration and allocation in the slicing planning, configuration, commissioning, and operation phases are analyzed. In sequence, we highlight the need for optimized architectural feature orchestration and recommend using ML-embed agents, federated learning intrinsic mechanisms for knowledge acquisition, and a data-driven approach embedded in the network slicing architecture. We further develop an architectural features orchestration case embedded in the SFI2 network slicing architecture. An attack prevention security mechanism is developed for the SFI2 architecture using distributed embedded and cooperating ML agents. The case presented illustrates the architectural feature's orchestration process and benefits, highlighting its importance for the network slicing process.

翻译：网络切片是下一代移动网络（NGMN）及车联网（IoV）、工业物联网（IIoT）等各类新兴系统的关键使能技术与发展趋势。编排与机器学习是网络切片流程中的核心要素——切片过程需要协调资源与功能，而机器学习可望优化编排流程。然而，现有网络切片架构缺乏在切片过程中定义智能方法以编排特征与资源的能力。本文探讨了网络切片架构中基于机器学习的特征与能力编排。首先分析了切片规划、配置、部署及运维阶段的资源编排与分配；继而强调优化架构特征编排的必要性，并提出在切片架构中嵌入ML智能体、联邦学习知识获取机制及数据驱动方法。我们进一步在SFI2网络切片架构中开发了特征编排案例，通过分布式嵌入协作ML代理实现攻击防御安全机制。该案例展示了架构特征编排的流程与优势，凸显其对网络切片流程的重要性。