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Dynamic edge server placement in mobile edge computing using modified red deer optimization algorithm and Markov game theory

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Abstract

Today, the development of intelligent mobile communication equipment, online applications such as online games, e-commerce, e-learning, and multimedia applications, and also the advent of the fifth generation of mobile communication networks have drawn mobile cloud services providers' special attention to this area. Cloud resources are brought to the edge of the network and adjacent to cloud users to increase the quality of the service provided. Proper placement of cloud resources reduces the resource access delay and balances the workload. Numerous studies have been conducted in this field. In most studies, it is assumed that the candidate location of the resources is fixed. The resources' area is also considered flat. Since the requested services of cloud users vary at different times of the day and in different geographical areas, the resources do not have the same efficiency during different times of the same day. For example, some administrative and business areas are active during the day, and at other times the demand decreases sharply. Thus, the mobility of cloud servers can be considered a suitable solution. In the proposed method, a two-stage static (SSP-RM) and dynamic (DSP-RM) model of cloud resource placement using Red deer (RD) and Markov game (MG) algorithms is introduced. Some cloud resources will be portable according to temporal and spatial requirements so that they can be transferred to different geographical areas according to different demands to increase their efficiency and distribute a more balanced workload on them while reducing latency. In the proposed method, by clustering the cloud resource deployment area and using the RD algorithm, the complexity of the cloud resource placement problem is reduced, and by using the Markov game model, the global convergence of resource deployment is obtained. Finally, dynamic cloud resource placement is performed using the Q-Learning (QL) algorithm. The results of experiments show that the proposed algorithm reduces latency, betters the load balance of cloud resources, and reduces the number of resources.

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Authors and Affiliations

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

    Ali Asghari & Alireza Vahdani

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

    Hossein Azgomi

  3. Institute for High-Performance Computing and Networking, National Research Council of Italy, c/o University of Calabria, Rende, CS, Italy

    Agostino Forestiero

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  1. Ali Asghari
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  2. Alireza Vahdani
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  3. Hossein Azgomi
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  4. Agostino Forestiero
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Correspondence to Ali Asghari.

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Asghari, A., Vahdani, A., Azgomi, H. et al. Dynamic edge server placement in mobile edge computing using modified red deer optimization algorithm and Markov game theory. J Ambient Intell Human Comput 14, 12297–12315 (2023). https://doi.org/10.1007/s12652-023-04656-z

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  • DOI: https://doi.org/10.1007/s12652-023-04656-z

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