# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 2 Related Works  
The server deployment problem in dynamic edge computing has received extensive attention in recent years, and scholars at home and abroad have carried out in-depth research from different perspectives. According to the differences in research objectives and methods, the existing works can be broadly classified into the following categories: deployment optimization based on task scheduling, edge server deployment based on drones, deployment strategies based on cost and load balancing, and deployment methods based on multi-objective optimization. In terms of task scheduling-based deployment optimization, reference [6] proposes a demand-responsive edge node deployment approach that considers service reliability and achieves high-quality task scheduling through rational planning of edge node deployment. However, this type of approach mainly focuses on task allocation and fails to fully consider the latency optimization of user requests. In terms of UAV-based edge server deployment, references [7,8] utilize UAVs as edge server nodes to provide computational offloading services for ground users, which significantly reduces the task response time. Despite the advantages of such approaches in complex terrain or emergency response scenarios, their deployment cost is high, and the greedy algorithm used makes it difficult to guarantee a globally optimal solution. Reference [9] further proposes a deployment algorithm based on K-means clustering, which takes the average system completion time as the performance index, but in edge computing scenarios, the completion time is not the most important core concern of users. In terms of deployment strategies based on cost and load balancing, references [10; 11] propose authentication-based cloud service deployment methods focusing on cost optimization, while reference [12] implements a load-balancing deployment scheme through genetic algorithms. However, none of these studies consider user request latency as a priority optimization objective. In terms of deployment methods based on multi-objective optimization, reference [13] transforms the edge server deployment problem into a multi-objective optimization problem, which balances factors such as operator's revenue, server provisioning cost, latency, and load balancing; references [14; 15; 16; 17] further combine online and offline policies, generate a deployment scheme by sensing the user mobility, and optimize the system by using improved genetic algorithms' and artificial bee colony algorithms' response times. Although these approaches have made significant progress in multi-objective optimization, they fail to adequately consider the impact of dynamic changes in user location and network heterogeneity on deployment results. In addition, references [18; 19] proposed deployment methods based on overlapping dominance chains and particle swarm optimization to minimize the deployment cost while maximizing the robustness of edge services but failed to delve into the collaborative mechanism of computational offloading among nodes. Although the above studies have made significant progress in edge server deployment, the following shortcomings still exist: (1) existing approaches mostly focus on task scheduling, cost optimization, or load balancing and fail to take user request latency as the core optimization objective; (2) in dynamic environments, the impacts of user mobility and network heterogeneity on the deployment strategy have not yet been adequately investigated; and (3) most of the heuristic algorithms (e.g., the genetic algorithm, particle swarm algorithm, etc.) have limitations in terms of global search capability and computational efficiency, making it difficult to quickly find an approximate optimal solution in complex networks. In this paper, we propose an edge server deployment strategy based on queuing search algorithm (QSA) to address the above problems.