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Multi-objective edge server placement using the whale optimization algorithm and game theory

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Abstract

Due to the users’ mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods.

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The authors declare that no funds, grants, or other support was received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Engineering, Shafagh Institute of Higher Education, Tonekabon, Iran

    Ali Asghari & Zahra darvishmofarahi

  2. Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran

    Hossein Azgomi

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  1. Ali Asghari
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  2. Hossein Azgomi
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  3. Zahra darvishmofarahi
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Asghari, A., Azgomi, H. & darvishmofarahi, Z. Multi-objective edge server placement using the whale optimization algorithm and game theory. Soft Comput 27, 16143–16157 (2023). https://doi.org/10.1007/s00500-023-07995-3

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