Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment

Author:

Singh Sushil KumarORCID,Salim Mikail MohammedORCID,Cha Jeonghun,Pan YiORCID,Park Jong HyukORCID

Abstract

Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.

Funder

Seoul National University of Science and Technology

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development

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3. Machine Learning Based 5G Network Slicing Management and Classification;2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC);2024-02-19

4. Enhancing Security and Privacy Implications in 5G Network Slicing;2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT);2024-01-11

5. Management Closed Control Loop Automation for Improved RAN Slice Performance by Subslicing;IEEE Access;2024

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