An Edge Server Placement Algorithm Based on Graph Convolution Network

Efficient edge server placement techniques select optimal locations for edge servers to improve network and energy performance. However, without prior knowledge of users' resource demands, the possibilities of optimal location for these edge servers within a network are vast, which is a challenging problem. On the other hand, most existing techniques ignore the influence of user mobility on user's resources demand, violation of low-latency, and high energy consumption of 5G networks. Therefore, this article addresses the edge server placement problem using network traffic to estimate the user resource demands. We first use a network traffic prediction model based on Graph Convolution Network to generate network traffic distribution. Second, the problem of edge server placement is formulated as a constraint optimization problem that places edge servers strategically to balance energy and latency. Searching randomly through many possible solutions and selecting the most descriptive optimal solutions can be time-consuming. Therefore, we used the particle swarm optimization (PSO) algorithm to optimize network delay and energy consumption, especially for high mobility areas. Experimental results are obtained to compare the performance of the proposed algorithm with existing methods. We evaluate the algorithm based on the real dataset from Shenzhen, Futian District. The results show our proposed algorithm averagely reduces edge servers' total cost and overloaded numbers by 23.98% and 52.71%, respectively.