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Joint Optimization of Edge Computing Server Deployment and User Offloading Associations in Wireless Edge Network via a Genetic Algorithm

Of late years, a growing number of mobile devices are demanding computation-intensive tasks with low delay requirements. Edge computing, where mobile devices offload their tasks to servers installed on the edge nodes of a network closer to the mobile devices, enables faster completion of the computation-intensive tasks. To offer low delay with a limited number of servers, efficient computing server deployment and user offloading associations resulting from the deployment are consequential. However, the existing research on server deployment and user offloading associations is restricted to heuristic algorithms failing to address joint optimization due to its prohibitive complexity. In contrast, this paper studies the joint design of computing server deployment and user offloading associations in wireless edge networks with wireless backhaul, enabling broadband transmission at a lower cost than the existing wired backhaul. Leveraging the evolutionary concept of a genetic algorithm, we devise a novel algorithm to solve the problem and minimize the average service delay while satisfying the delay requirements of individual users. The simulation results show that the proposed algorithm outperforms the conventional random search or heuristic algorithms.