# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 1 Introduction  
With the iteration of the Internet and the continuous upgrading and remodeling of communication technology, the use of mobile devices is becoming increasingly convenient. The statistical report on the development of China's Internet network shows that as of December 2023, the number of Internet users in China reached 1.091 billion, with an increase of 25.62 million users compared to December 2022. Moreover, the proportion of Internet users accessing the Internet through mobile phones was 99.9% [1]. However, mobile terminal devices face many limitations, such as battery life, limited computing power, and more. To address these issues and improve the performance of mobile applications, it is common practice to forward requests from mobile terminals to cloud computing centers for processing. Cloud computing connects a large number of independent computing units through high-speed networks, providing users with high-performance computing resources or services at any time [2]. However, cloud computing centers are often located far from users, making it difficult to promptly respond to requests from mobile terminal devices. In scenarios such as drones and autonomous driving, where latency requirements are extremely stringent, cloud computing gradually fails to meet user demands. To effectively address this issue, mobile edge computing (MEC) has emerged. MEC can be understood as operating a cloud server at the edge of a mobile network, which handlestasks that traditional network infrastructures cannot, such as machine-to-machine (M2M) gateways, control functions, intelligent video acceleration, etc. MEC operates at the edge of the network and does not logically rely on the rest of the network, which is crucial for applications with high-security requirements. MEC servers possess high computing power, which is suitable for analyzing and processing large amounts of data. At the same time, because MECs are geographically close to users, network response times to users are reduced, and the possibility of network congestion in the transmission and core networks is minimized. MECs located at the edge of the network can also obtain the base station's identity document (ID), available bandwidth, and other network- and user-related information in real time, enabling link-aware adaptation, which greatly enhances the user's quality of service experience [3]. With the increasing interest in MEC research in both academia and industry in recent years, a large number of research results have emerged [4]. However, most existing studies focus on summarizing MEC research from a single aspect, such as computation offloading, resource allocation, security and privacy, etc. Yet, MEC is a distributed computing architecture with a relatively low-cost budget, so its computational and storage resources cannot be scaled up indefinitely. Therefore, MEC-related research still faces many challenges, such as how to allocate limited resources to maximize user efficiency and designing a reasonable task placement strategy along with resource allocation and server placement locations [5]. In mobile edge computing, an effective edge server placement strategy is key to solving the problem of limited resources at edge servers, such as network, computation, and cache, as well as the diversity and differences in users' demands. These factors can lead to unbalanced loads on mobile edge servers and a degradation of the users' quality of service. Based on the aforementioned analysis, this study proposes a server deployment optimization strategy based on the queuing search algorithm (QSA). This strategy innovatively employs the number of network hops as an abstract representation of latency, aiming to minimize the number of hops to optimize user request forwarding. Under ideal conditions, where factors such as transmission latency in real-world network environments are not considered, this strategy can effectively determine the optimal deployment scheme for minimizing user latency.