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Hsieh, Cheng-Ying, Phung-Duc, Tuan, Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719 and Chen, Jyh-Cheng
(2023)
Design and analysis of dynamic block-setup reservation algorithm for 5G network slicing.
IEEE Transactions on Mobile Computing, 22 (9).
pp. 5140-5154.
ISSN 1536-1233
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Abstract
In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 13 May 2022 10:31 |
| Last Modified: | 11 Aug 2023 09:30 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/85018 |
| DOI: | 10.1109/TMC.2022.3169034 |
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