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.

零触式网络和服务管理（ZSM）框架是第五代（5G）及其之后（5G+）网络管理的新兴范式，通过自动化的自管理和自愈能力应对现代网络的日益复杂和不断增长的数据量。该研究通过综述零触式网络（ZTNs）的领域，探讨了 ZSM 框架中的网络优化、流量监控、能源效率和安全性等方面。研究发现，将自动化机器学习（AutoML）应用于 ZTNs 中，可以降低网络管理成本并提高性能。实验结果表明，AutoML 对于应用吞吐量的预测具有卓越的准确性。将 AutoML 与 ZSM 概念集成，显著降低了网络配置和管理工作量，使运营商能够更多地投入时间和资源到其他重要任务。该研究还提供了一个集成 AutoML 和 ZSM 概念的高层 5G 系统架构。研究突显了 ZTNs 和 AutoML 在改进 5G + 网络管理方面的潜力，实现自动化决策，并使网络运营商能够实现更高的效率、更好的性能和更优化的用户体验。