Al-Quzweeni, AN, Lawey, AQ orcid.org/0000-0003-3571-4110, Elgorashi, TEH et al. (1 more author) (2019) Optimized Energy Aware 5G Network Function Virtualization. IEEE Access, 7. pp. 44939-44958. ISSN 2169-3536
Abstract
In this paper, network function virtualization (NVF) is identified as a promising key technology that can contribute to energy-efficiency improvement in 5G networks. An optical network supported architecture is proposed and investigated in this work to provide the wired infrastructure needed in 5G networks and to support NFV towards an energy efficient 5G network. In this architecture the mobile core network functions as well as baseband function are virtualized and provided as VMs. The impact of the total number of active users in the network, backhaul/fronthaul configurations and VM inter-traffic are investigated. A mixed integer linear programming (MILP) optimization model is developed with the objective of minimizing the total power consumption by optimizing the VMs location and VMs servers’ utilization. The MILP model results show that virtualization can result in up to 38% (average 34%) energy saving. The results also reveal how the total number of active users affects the baseband virtual machines (BBUVMs) optimal distribution whilst the core network virtual machines (CNVMs) distribution is affected mainly by the inter-traffic between the VMs. For real-time implementation, two heuristics are developed, an Energy Efficient NFV without CNVMs inter-traffic (EENFVnoITr) heuristic and an Energy Efficient NFV with CNVMs inter-traffic (EENFVwithITr) heuristic, both produce comparable results to the optimal MILP results. Finally, a Genetic algorithm is developed for further verification of the results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This article is protected by copyright and is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ |
Keywords: | 5G networks; Backhaul; BBU; Energy Efficiency; Fronthaul; Genetic Algorithm; IP over WDM; Network Function Virtualization; NFV |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/H040536/1 EPSRC EP/K016873/1 EPSRC EP/S016570/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Mar 2019 16:16 |
Last Modified: | 25 Jun 2023 21:45 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/ACCESS.2019.2907798 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143796 |