Multi-Objective edge server placement using the whale optimization algorithm and Game theory

Abstract

Abstract With the emergence of the fifth-generation technology of telecommunication networks, the rapid growth of user's mobile equipment, as well as the emergence of new applications such as the Internet of Things, online education, e-commerce, multimedia applications, and social networks, a new paradigm has been opened in the field of information technology. 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 meet these new services. Therefore, mobile cloud computing was quickly formed. Cloud service providers improved the quality of their services by moving some of the servers to the edge of the network and in the vicinity of mobile users. In this regard, several architectures and protocols of mobile cloud computing models have been introduced by some researchers. Considering the mobility nature of users and the heterogeneous service demands in different areas, the optimal placement of these resources plays an important role in increasing the quality of service provided to users. However, due to a large number of servers, finding the optimal location of all servers is considered 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-areas to reduce the complexity of the problem. Then, by using the WOA algorithm, the search begins to find the optimal location of the servers. Then, the neural network is used for the local placement of all servers in each area. And finally, in the last step, game theory is deployed for the convergence of resource placement in all zones. The experimental results show that the proposed method better balances the workload on resources and reduces access latency and the number of servers, compared with some similar and state-of-the-art algorithms.