Network slice selection in softwarization-based mobile networks

Zhao, G., Qin, S., Feng, G. and Sun, Y. (2020) Network slice selection in softwarization-based mobile networks. Transactions on Emerging Telecommunications Technologies, 31(1), e3617. (doi: 10.1002/ett.3617)

Abstract

Recently, network slicing (NS) has been introduced as a key enabler to accommodate diversified services in network functions virtualization–enabled software‐defined mobile networks. Although there has been some research work on network slice deployment and configuration, how user equipments select the most appropriate network slice is still an essential yet challenging issue, as slice selection may substantially affect the resource utilization and user Quality of Service (QoS). In this paper, we investigate the optimal selection of end‐to‐end slices with the aim of improving network resources utilization while guaranteeing the QoS of users. We formulate the optimal slice selection problem as maximizing the users satisfaction degree and prove it is NP‐hard. We thus resort to genetic algorithm (GA) to find a suboptimal solution and develop a GA‐based heuristic algorithm. The effectiveness of our proposed NS selection algorithm is validated via simulation experiments.

• Item Type: Articles
• Additional Information: Funding: National Science Foundation of China. Grant Numbers: 61631004, 61871099.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao and Feng, Professor Gang
• Authors: Zhao, G., Qin, S., Feng, G., and Sun, Y.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Transactions on Emerging Telecommunications Technologies
• Publisher: Wiley
• ISSN: 2161-3915
• ISSN (Online): 2161-3915
• Published Online: 25 April 2019
• Copyright Holders: Copyright © 2019 John Wiley and Sons
• First Published: First published in Transactions on Emerging Telecommunications Technologies 31(1):e3617
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher