# Zero-Touch Networks: Towards Next-Generation Network Automation

**Authors:** Mirna El Rajab, Li Yang, Abdallah Shami  
**arXiv:** [2312.04159](https://arxiv.org/abs/2312.04159)  
**Date:** 2023-12-07  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2312.04159v1/

## Key points

- The Zero-Touch Network and Service Management (ZSM) framework is an emerging paradigm in the management of 5G+ networks.
- ZSM offers automated self-management and self-healing capabilities to address network complexity and data volume.
- ZSM frameworks leverage advanced technologies like Machine Learning (ML) for intelligent decision-making and reduced human intervention.
- This paper focuses on Zero-Touch Networks (ZTNs) within the ZSM framework, specifically network optimization, traffic monitoring, energy efficiency, and security aspects.
- Challenges associated with ML in ZSM are explored, requiring diverse network automation solutions.
- Automated ML (AutoML) is investigated for reducing network management costs and enhancing performance in ZTNs.
- Experimental results show that the proposed AutoML pipeline outperforms traditional ML in prediction accuracy.
- Integrating AutoML and ZSM concepts can significantly reduce network configuration and management efforts.
- A high-level 5G system architecture incorporating AutoML and ZSM concepts is provided.
- ZTNs combined with Automated ML have the potential to revolutionize network automation processes for optimal performance in 5G+ networks.

## Summary

The Zero-Touch Network and Service Management (ZSM) framework is an emerging paradigm in the management of fifth-generation (5G) and Beyond (5G+) networks. It offers automated self-management and self-healing capabilities to address the increasing complexity and data volume of modern networks. ZSM frameworks leverage advanced technologies like 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, focusing on network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks. The study explores the challenges associated with ZSM, particularly those related to ML which require diverse network automation solutions. To reduce network management costs and enhance performance, the research investigates the application of Automated ML (AutoML) in ZTNs. AutoML automates the selection and tuning process of an ML model for a given task. Specifically, it focuses on AutoML's ability to predict application throughput and autonomously adapt to data drift. Experimental results demonstrate that the proposed AutoML pipeline outperforms traditional ML in terms of prediction accuracy. By integrating AutoML and ZSM concepts, significant reductions in network configuration and management efforts can be achieved. This allows 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 by enabling automated decision-making. It empowers network operators to achieve higher efficiency, improved performance, enhanced user experience as well as reduced human intervention requirements for network automation tasks such as configuration or maintenance activities. Overall, this study emphasizes how Zero-Touch Networks within the ZSM framework combined with Automated ML have the potential to transform network automation processes while ensuring optimal performance levels for 5G+ networks.