The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks. ZSM frameworks leverage advanced technologies such as Machine Learning (ML) to enable intelligent decision-making and reduce human intervention. This paper presents a comprehensive survey of Zero-Touch Networks (ZTNs) within the ZSM framework, covering network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks. The paper explores the challenges associated with ZSM, particularly those related to ML, which necessitate the need to explore diverse network automation solutions. In this context, the study investigates the application of Automated ML (AutoML) in ZTNs, to reduce network management costs and enhance performance. AutoML automates the selection and tuning process of a ML model for a given task. Specifically, the focus is on AutoML's ability to predict application throughput and autonomously adapt to data drift. Experimental results demonstrate the superiority of the proposed AutoML pipeline over traditional ML in terms of prediction accuracy. Integrating AutoML and ZSM concepts significantly reduces network configuration and management efforts, allowing operators to allocate more time and resources to other important tasks. The paper also provides a high-level 5G system architecture incorporating AutoML and ZSM concepts. This research highlights the potential of ZTNs and AutoML to revolutionize the management of 5G+ networks, enabling automated decision-making and empowering network operators to achieve higher efficiency, improved performance, and enhanced user experience.

翻译：零接触网络与服务管理（ZSM）框架代表了第五代（5G）及超越（5G+）网络管理中的新兴范式，通过提供自动化的自管理和自愈能力，以应对现代网络日益增长的复杂性与数据量。ZSM框架利用机器学习（ML）等先进技术实现智能决策并减少人工干预。本文对ZSM框架下的零接触网络（ZTNs）进行了全面综述，涵盖下一代网络的优化、流量监控、能效及安全等方面。研究探讨了ZSM面临的挑战（尤其是与ML相关的挑战），这促使需要探索多样化的网络自动化解决方案。在此背景下，本文研究了自动化机器学习（AutoML）在ZTNs中的应用，以降低网络管理成本并提升性能。AutoML能针对给定任务自动完成ML模型的选择与调优过程。具体而言，本文重点分析了AutoML预测应用吞吐量并自主适应数据漂移的能力。实验结果表明，所提出的AutoML流水线在预测精度上优于传统ML方法。集成AutoML与ZSM概念可显著减少网络配置与管理工作量，使运营商能将更多时间与资源分配给其他重要任务。本文还提出了融合AutoML与ZSM概念的高层级5G系统架构。本研究凸显了ZTNs与AutoML在革新5G+网络管理方面的潜力，可实现自动化决策，助力网络运营商达成更高效率、更优性能及增强的用户体验。