Optimal 5G network sub-slicing orchestration in a fully virtualised smart company using machine learning
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Affiliation
University of BedfordshireIssue Date
2025-02-06Subjects
reinforcement learningresource allocation
traffic prediction
network slice orchestration
5G networks
supervised learning
network slicing
resource management
machine learning
Subject Categories::G760 Machine Learning
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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.Citation
Efunogbon A, Liu E, Qiu R, Efunogbon T (2025) 'Optimal 5G network sub-slicing orchestration in a fully virtualised smart company using machine learning', Future Internet, 17 (2), 69Journal
Future InternetAdditional Links
https://www.mdpi.com/1999-5903/17/2/69Type
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enEISSN
1999-5903Scopus Count
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