Review on Network Slicing Optimization: A Machine Learning Perspective

DOI

10.2991/978-94-6463-716-8_71How to use a DOI?

Keywords

AI; ML; QoS; Network Slicing; and Software Defined Networking (SDN)

Abstract

Investigating how network slicing affects resource allocation and management, the paper explores methods for optimizing network resources in response to current demand and traffic trends. The effect of network slicing on Quality of Service (QoS) measurements is also examined, along with the ways in which different applications and consumers might receive distinct services inside the same infrastructure. Additionally, examined is how edge computing and cloud-native architectures enable network slicing features, emphasizing how crucial they are to providing high-bandwidth and low-latency services. The study also addresses the legal structures and regulatory issues that control the implementation and functioning of network slicing in 5G networks. It emphasizes the need for standardized interfaces and protocols to enable interoperability between different network slices and ensure seamless integration with existing network infrastructures. It advocates for the importance of continued research and development efforts in artificial intelligence (AI), machine learning (ML), and security to realize the promise of network slicing as a key enabler of future digital ecosystems.In conclusion, the paper underscores the transformative impact of network slicing in unlocking the full potential of 5G networks for diverse applications and industries.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Margi Patel
AU  - Nitin Rathore
AU  - Ramesh R. Naik
AU  - Sashrik Gupta
AU  - Vinod Patel
PY  - 2025
DA  - 2025/05/26
TI  - Review on Network Slicing Optimization: A Machine Learning Perspective
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 957
EP  - 971
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_71
DO  - 10.2991/978-94-6463-716-8_71
ID  - Patel2025
ER  -