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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  \nCopyright: © 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/)) .  \nSchool of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;  \n[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.)  \n* 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.)  \nAbstract: 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.  \nKeywords: 5G networks; network slicing; network slice orchestration; resource management; resource allocation; machine learning; supervised learning; reinforcement learning; traffic prediction  \n1. Introduction  \nMachine 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.  \nThis paper addresses these challenges by proposing a novel ONSSO framework to autonomously orchestrate 5G network slices optimally over a mobile network. This is","cbCaiuci9lVdIa8d","https://ap.wps.com/l/cbCaiuci9lVdIa8d","pdf",5683511,22,"English","# Introduction\n## Background on 5G slicing and sub-slicing\n## Proposed ONSSO framework and architecture\n# Literature Review\n## 5G Network Slicing\n## Machine learning for slice optimisation","[{\"question\":\"What is ONSSO in this paper?\",\"answer\":\"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.\"},{\"question\":\"How does the Enhanced Sub-Slice (eSS) model contribute?\",\"answer\":\"eSS enables granular resource allocation within the network slicing architecture. This reduces service denial risks and improves user experience.\"},{\"question\":\"How was the proposed framework evaluated?\",\"answer\":\"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.\"}]","Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning | PDF",55]