Optimizing Task Migration for Public and Private Services in Vehicular Edge Networks: A Dual-Layer Graph Neural Network Approach

In the vehicular edge networks (VEN), task migration is complicated by issues like vehicle movement, diverse resource allocation, and integrating sensing with communication technologies. This paper presents a task migration strategy to optimize task flow under limited resources in PMN-assisted VEN. Vehicles can send public and private tasks to roadside units (RSUs), constrained by bandwidth, computational power, and storage space. Public tasks aim at data collection for road transportation management, while private tasks cover a spectrum of services from work to entertainment. To address the limitations imposed by resource scarcity and meet the demands of task migration, we have developed a dual-layer graph neural network (GNN) that leverages vehicle mobility patterns. In particular, the first layer of GNN acquires vehicle information and the latest surrounding information, and sends it to the nearby RSU. Considering the variety of tasks and multi-dimensional resource constraints, the second GNN layer forecasts RSU resource availability and vehicular trajectories. Subsequently, a task-based maximum flow algorithm (T-MFA) is proposed to refine task migration paths and resource allocation strategies to maximize task flow. Simulation experiments validate the efficacy of the proposed algorithm, demonstrating its capability to achieve optimal task migration by accommodating differences in tasks, resources, and capacities.