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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.
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