Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning

Abimbola Efunogbon (), Enjie Liu (), Renxie Qiu and Taiwo Efunogbon
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Abimbola Efunogbon: School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK
Enjie Liu: School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK
Renxie Qiu: School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK
Taiwo Efunogbon: School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK

Future Internet, 2025, vol. 17, issue 2, 1-22

Abstract: This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. We propose Enhanced Sub-Slice (eSS), a machine learning pipeline that enables granular resource allocation through network sub-slicing, reducing service denial risks and enhancing user experience. This leads to the introduction of Company Network as a Service (CNaaS), a new enterprise service model for mobile network operators (MNOs). The framework was evaluated using Google Colab for machine learning implementation and MATLAB/Simulink for dynamic testing. The results demonstrate that ONSSO improves MNO collaboration through real-time resource information sharing, reducing orchestration delays and advancing adaptive 5G network management solutions.

Keywords: 5G networks; network slicing; network slice orchestration; resource management; resource allocation; machine learning; supervised learning; reinforcement learning; traffic prediction (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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