A Geographic Space-Oriented Search Algorithm for the Robust Placement of Edge Servers

To address the challenges posed by exponential data growth, mobile edge computing (MEC) has emerged as a key solution by decentralizing server resources from the cloud to the network edge near mobile users, thereby facilitating high-quality and low-latency service delivery. However, failures often occur in real-world mobile networks, making it crucial to consider network robustness in addition to user coverage when deploying edge servers in an MEC network. In this paper, the Geographic space-oriented Search algorithm for Edge Server Placement (ESP-GS) is proposed to optimize both of these objectives. The core idea behind ESP-GS is to leverage a technique known as “Constraint Relaxation” to downscale the edge server placement from an m-dimensional combinatorial problem to a k-dimensional continuous problem in the geographic space. This approach significantly enhances scalability, making it well-suited for large-scale network deployments. Furthermore, since the robustness of mobile edge networks lacks a widely accepted metric, a Random Failure Evaluation (RFEval) method and two corresponding metrics are designed to assess failure tolerance in practical scenarios. Extensive comparative experiments have been conducted on real-world and publicly available datasets. The results show that the ESP-GS algorithm exhibits excellent performance in both user coverage and network robustness, improving the overall network performance by 8% to 27% compared to other benchmark algorithms.