Task Unloading Algorithm for Mobile Edge Computing based on graph neural network

As a cutting-edge and visionary computing mode, edge computing is expected to significantly improve the computing performance of mobile devices (MDs) and improve user experience. This improvement is due to the shift of compute-intensive tasks from MDs with limited computing power to the more well-resourced cloud. However, the task unloading process also faces the limitation of limited communication bandwidth and server resource usage. The task of minimizing the total overhead of the system is especially difficult when the application needs to be completed in a certain amount of time. To overcome these obstacles, our research proposes a novel task offloading and resource allocation strategy based on graph neural networks (GNN). Our strategy can assist in decision-making on computing task offloading, controlling CPU clock frequency, and allocating communication capabilities. To verify the validity of our approach, we conducted a series of simulation experiments in different scale edge computing scenarios. The experimental results show that the task unloading optimization algorithm based on graph neural network can guarantee the quality of service (QoS) and manage the whole system resource cost reasonably. Therefore, our study provides a substantial basis for further exploration of GCN’s potential in the field of edge computing task offloading decision making.