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Energy-Efficient Virtual Resource Allocation of Slices in Vehicles-Assisted B5G Networks

Cao, Haotong and Zhao, Haitao and Jindal, Anish and Aujla, Gagangeet Singh and Yang, Longxiang (2022) 'Energy-Efficient Virtual Resource Allocation of Slices in Vehicles-Assisted B5G Networks.', IEEE Transactions on Green Communications and Networking .


Academia community started the research beyond 5G (B5G) while 5G systems and networks are still being landed for large-scale commercial applications. In order to enhance the agility and flexibility attributes of B5G networks, network function virtualization (NFV) and network slicing (NS) are attracting extensive research attention. Meanwhile, vehicles are promised to connect to the B5G networks so as to expand the service coverage and reach the ‘last one mile’. In this paper, we research the virtual resource allocation of slices in vehicles-assisted B5G networks. We aim at saving total energy cost of deployed slices while ensuring high slice acceptance ratio. We firstly present the system model of vehicles-assisted B5G networks, supporting both virtualization and slicing schemes. Then, we present the energy cost of vehicles-assisted B5G networks. Afterwards, we propose one energy efficient algorithm, abbreviated as Ener-Eff-Slice, to solve the virtual resource allocation of slices in vehicles-assisted B5G networks. Numerical results are recorded, plotted and discussed, which prove the efficacy of our scheme. Finally, we do the conclusion marks and discuss the next-step work.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:06 May 2022
Date of first online publication:14 March 2022
Date first made open access:06 May 2022

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