Joint Task Offloading and Resource Allocation in Software-Defined Networking-Enabled Vehicular Edge Computing: A Multi-Objective Approach

Vehicular Edge Computing (VEC) enables vehicles to offload computation tasks to edge servers, effectively addressing computing resource constraints faced by vehicles. However, the efficient scheduling of network edge computing resources is crucial in VEC. In this work, we investigate task offloading and resource allocation in Software-Defined Networking (SDN)-enabled VEC to minimize delay and cost by efficiently utilizing resources of vehicles and edge servers. Specifically, we propose a computation offloading architecture in SDN-enabled VEC, where resource-constrained vehicles can offload computation tasks to edge servers. Furthermore, we formulate a joint task offloading and resource allocation problem, which is a non-linear programming problem. Inspired by the advantages of particle swarm optimization (PSO), we propose a PSO-based task offloading and resource allocation algorithm that searches for Pareto-optimal solutions. Finally, we build a simulation model and evaluate the performance comprehensively. The simulation results exhibit the effectiveness of the proposed scheme.