4.7 Article

Resource allocation for cost minimization of a slice broker in a 5G-MEC scenario

期刊

COMPUTER COMMUNICATIONS
卷 213, 期 -, 页码 331-344

出版社

ELSEVIER
DOI: 10.1016/j.comcom.2023.11.016

关键词

Resource allocation; Slice broker; MEC; Edge computing; 5G; Cost minimization

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This article explores the concept of a Slice Broker, an intermediate entity that purchases resources from Infrastructure Providers to offer customized network slices to users. The article proposes a cost-minimization problem and compares it with alternative problems to demonstrate its effectiveness and cost-saving capabilities.
The fifth generation (5G) of mobile networks may offer a custom logical and virtualized network called network slicing. This virtualization opens a new opportunity to share infrastructure resources and encourage cooperation between several Infrastructure Providers (InPs) to offer tailored network slices for the Slice Tenants (STs). The Slice Broker (SB) is emerging as intermediate entity that purchases resources from the InPs and it offers network slices to the STs. The main challenge of the SB is to jointly decide the purchase of heterogeneous (data and network) resources from multiple InPs and create the slices to meet the various requests from the STs. Being an economical entity, the target of the SB is to maximize its profit by minimizing the costs while satisfying all the ST requests. This paper formulated the SB cost minimization problem and used CPLEX to obtain the optimal solution. The problem formulation considers the realistic scenario that the InPs offer the computing, storage and network resources by using predetermined configurations. Therefore, for each of the computing platform and logical connection, the SB may select one of the configurations. The proposed cost-minimization problem is compared with three alternative problems that have three different objectives: computing platform consolidation, network connection consolidation, and both computing-network consolidation. The computing platform and network connection consolidation are currently the most common approaches for decreasing resource costs. However, the result shows that consolidating computing and network resources fails to reach the actual minimal cost. The proposed problem finds the cheapest solution, which can save at least 30% of the total cost of the other approaches in every evaluated scenario. Moreover, consolidating the number of computing platforms can lead to the most expensive solution, up to 40% higher than the optimal solution of our proposed problem.

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