{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:43:59Z","timestamp":1771307039245,"version":"3.50.1"},"reference-count":17,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T00:00:00Z","timestamp":1738800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17020069","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T08:53:41Z","timestamp":1738832021000},"page":"69","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning"],"prefix":"10.3390","volume":"17","author":[{"given":"Abimbola","family":"Efunogbon","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4617-595X","authenticated-orcid":false,"given":"Enjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}]},{"given":"Renxie","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}]},{"given":"Taiwo","family":"Efunogbon","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"ref_1","first-page":"772","article-title":"Machine Learning for 5G Network Slicing and Orchestration: Opportunities, Challenges, and Solutions","volume":"24","author":"Jiang","year":"2022","journal-title":"IEEE Commun. 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