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

Sushil Kumar Singh, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan and Jong Hyuk Park
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Sushil Kumar Singh: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Mikail Mohammed Salim: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Jeonghun Cha: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Yi Pan: Department of Computer Science, Georgia State University, Atlanta, GA 30302-5060, USA
Jong Hyuk Park: Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea

Sustainability, 2020, vol. 12, issue 15, 1-23

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.

Keywords: machine learning; network sub-slicing; sustainable 5G environment; IoT (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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