A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing

In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for computing power to be spread out. This lets interactive services respond quickly. Nevertheless, the computational resources at edge servers are inherently limited and often tasked with handling multiple concurrent operations. The inefficacious allocation of these resources significantly impairs the efficiency of task offloading. Additionally, indiscriminate offloading could overwhelm the servers, detrimentally impacting the performance of subsequent tasks. To circumvent these challenges, this study introduces a multi-tier offloading model predicated on game theory principles. This framework aims to optimize resource utilization at the edge while accounting for server load to ensure the timely execution of latency-sensitive tasks. To evaluate this model, this paper created a simulation environment specifically for video game tasks in consumer electronics. The experimental results show that the multi-tier offloading model can effectively relieve the load pressure on the edge server. The task failure rate of the multi-tier offloading model remains at the lowest level compared with several state-of-the-art algorithms, significantly reducing the execution delay of tasks and being able to meet the requirements of consumer electronics applications.