Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning
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 n...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/2/69 |
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| author | Abimbola Efunogbon Enjie Liu Renxie Qiu Taiwo Efunogbon |
| author_facet | Abimbola Efunogbon Enjie Liu Renxie Qiu Taiwo Efunogbon |
| author_sort | Abimbola Efunogbon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-daa37bbfd5564f1c93024fdb4f1248c4 |
| institution | DOAJ |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-daa37bbfd5564f1c93024fdb4f1248c42025-08-20T03:11:21ZengMDPI AGFuture Internet1999-59032025-02-011726910.3390/fi17020069Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine LearningAbimbola Efunogbon0Enjie Liu1Renxie Qiu2Taiwo Efunogbon3School of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKSchool of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKSchool of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKSchool of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UKThis 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.https://www.mdpi.com/1999-5903/17/2/695G networksnetwork slicingnetwork slice orchestrationresource managementresource allocationmachine learning |
| spellingShingle | Abimbola Efunogbon Enjie Liu Renxie Qiu Taiwo Efunogbon Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning Future Internet 5G networks network slicing network slice orchestration resource management resource allocation machine learning |
| title | Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning |
| title_full | Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning |
| title_fullStr | Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning |
| title_full_unstemmed | Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning |
| title_short | Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning |
| title_sort | optimal 5g network sub slicing orchestration in a fully virtualised smart company using machine learning |
| topic | 5G networks network slicing network slice orchestration resource management resource allocation machine learning |
| url | https://www.mdpi.com/1999-5903/17/2/69 |
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