Efficient Revenue-Based MEC Server Deployment and Management in Mobile Edge-Cloud Computing

• Published in: IEEE/ACM transactions on networking Vol. 31; no. 4; pp. 1449 - 1462
• Main Authors: Zhang, Yongmin, Wang, Wei, Ren, Ju, Huang, Jinge, He, Shibo, Zhang, Yaoxue
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applications programs; Cloud computing; Costs; Edge computing; Maximization; MEC server deployment; Mobile applications; Mobile computing; Mobile edge computing; Optimization; profit maximization; Profitability; Quality of service; Resource management; resource placement; Revenue; Servers; Task analysis
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2022.3217280

With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.