Optimization of Accuracy-Sensitive Task Offloading and Model Update in Vehicular Edge Computing

Vehicular edge computing (VEC) enables vehicles to offload computation-intensive tasks to roadside units (RSUs) equipped with deep learning (DL) models, thereby supporting low-latency and accuracy-sensitive intelligent vehicular tasks. To adapt DL models to evolving task requirements and time-varying vehicular environments, the RSUs must consume limited computing and memory resources to retrieve optimized parameters from the cloud to update local models. During these updates, the DL models cannot provide services to tasks, and vice versa. However, the limited computational and memory resources of RSUs make it challenging to determine which tasks to offload and which DL models to update, in order to maximize task acceptance rates and quality of service. In this paper, we investigate the joint optimization of accuracy-sensitive task offloading and DL model updating in VEC systems. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem that aims to maximize a weighted utility function of task acceptance rate (AR) and quality of service (QoS), subject to latency, accuracy, and resource constraints. The formulated problem is shown to be NP-hard. To enable efficient decision making, we propose a heuristic algorithm termed the Load-Accuracy-Sensitive Joint Task Offloading and Model Update algorithm. The proposed algorithm leverages real-time system state information and jointly considers transmission feasibility, RSU workload, model accuracy matching, and queue-aware load balancing when making task offloading and model update decisions. Extensive simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms.