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

Sep 17, 2026

Uploaded by Maeve

This paper presents Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a machine learning framework for dynamic, autonomous orchestration of 5G network slices. It uses LazyPredict to select supervised learning algorithms automatically from real-time network conditions and historical data, and proposes Enhanced Sub-Slice (eSS) to enable granular resource allocation. The approach introduces Company Network as a Service (CNaaS) for mobile network operators, evaluated via Google Colab and MATLAB/Simulink, showing improved latency, bandwidth, throughput, jitter, packet loss, and resource utilization while reducing orchestration delays and strengthening collaboration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Background on 5G slicing and sub-slicing

Proposed ONSSO framework and architecture

Literature Review

5G Network Slicing

Machine learning for slice optimisation

Article Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning Abimbola Efunogbon *, Enjie Liu *, Renxie Qiu and Taiwo Efunogbon Academic Editor: Sachin Sharma Received: 4 November 2024 Revised: 20 December 2024 Accepted: 1 January 2025 Published: 6 February 2025 Citation: Efunogbon, A.; Liu, E.; Qiu, R.; Efunogbon, T. Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning. Future Internet 2025, 17, 69. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)fi17020069 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK; [renxi.qiu@beds.ac.uk](renxi.qiu@beds.ac.uk) (R.Q.); [taiwo.efunogbon1@study.beds.ac.uk](taiwo.efunogbon1@study.beds.ac.uk) (T.E.) * Correspondence: [abimbola.efunogbon@study.beds.ac.uk](abimbola.efunogbon@study.beds.ac.uk) (A.E.); [enjie.liu@beds.ac.uk](enjie.liu@beds.ac.uk) (E.L.) 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 1. Introduction Machine learning is pivotal in enhancing 5G network slicing and orchestration, offering innovative solutions to complex challenges, and unlocking new opportunities for network optimisation [1] . Fifth-generation (5G) cellular networks aim to transform mobile broadband communication by delivering unprecedented data rates, quality of service, connectivity, and flexibility in mobile networks [2] . Network slicing introduced a paradigm shift in mobile broadband communications, creating multiple virtual verticals from end to end while sharing the available physical infrastructure [3] . With network sub-slicing, these slices are further divided into granular sub-slices dedicated to a specific service, tenant, or application instance. This available network resource is managed and allocated as required to each service or user demanding a network slice, helping to reduce capital expenditure, a reputed challenge in current 5G network slice orchestration. Some of the standard network slices include ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) [4] . As the demand for these slices continues to grow, existing methods for resource allocation will struggle to meet performance expectations. This paper addresses these challenges by proposing a novel ONSSO framework to autonomously orchestrate 5G network slices optimally over a mobile network. This is

FAQs

What is ONSSO in this paper?

ONSSO is a machine learning framework for dynamic and autonomous 5G network slice orchestration. It selects learning algorithms based on real-time network conditions and historical data.

How does the Enhanced Sub-Slice (eSS) model contribute?

eSS enables granular resource allocation within the network slicing architecture. This reduces service denial risks and improves user experience.

How was the proposed framework evaluated?

The framework was implemented using Google Colab for machine learning and tested with MATLAB/Simulink for dynamic scenarios. Results show improvements in latency, throughput, jitter, packet loss, and resource utilization versus conventional methods.

Sep 17, 2026

Uploaded by Maeve