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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abimbola Efunogbon & Enjie Liu & Renxie Qiu & Taiwo Efunogbon, 2025. "Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning," Future Internet, MDPI, vol. 17(2), pages 1-22, February.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:2:p:69-:d:1584963
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