Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm

Edge computing, characterized by its proximity to users and fast response times, is considered one of the key technologies for addressing low-latency demands in the future. An appropriate edge server deployment strategy can reduce costs for service providers and improve the quality of service for users. However, most previous studies have focused on server coverage or deployment solution consumption time, often neglecting the most critical aspect: minimizing user-request response latency. To address this, we propose an edge deployment strategy based on the queuing search algorithm (QSA), which models the edge deployment problem as a multi-constrained nonlinear optimization problem. The QSA mimics the logic of human queuing behavior and has the ability to perform faster global searches while avoiding local optima. Experimental results show that, compared to the genetic algorithm, simulated annealing algorithm, particle swarm optimization, and other recent algorithms, the average number of “hopping” iterations in QSA is 0.1 to 0.6 times fewer than in the other algorithms. Additionally, QSA is particularly suitable for edge computing environments with a large number of users and devices.