A Cooperative Kernel-Based Method for Task Offloading in Vehicular Edge Computing

Vehicular Edge Computing (VEC) has emerged as a promising paradigm for supporting real-time applications by deploying communication and computation resources at the network edge. However, existing task offloading strategies often suffer from performance degradation within VEC systems, where offloading outcomes are difficult to predict accurately due to fluctuating environmental parameters that are challenging to obtain in real-time. Moreover, current offloading methods rely on learning-based approaches, necessitating extensive training efforts to adapt to diverse vehicular applications. Accordingly, we investigate the Cooperative Task Offloading (CTO) problem by considering dynamic nature of vehicular environment, which aims to minimize overall task completion time. We reformulate CTO as a cooperative contextual multi-armed bandit problem and propose a Cooperative Kernel-based Server Selection (CK-SS) algorithm, which facilitates offloading decisions by enabling online reward estimation through information sharing among vehicles. Specifically, we develop a composite kernel function that captures both task characteristics and temporal correlations among historical context-action pairs and an efficient rule for online parameter update. The reward under a given context is estimated based on Gaussian Process and the action is determined using Upper Confidence Bound (UCB) policy. Finally, we implement a simulation model and comprehensive simulation results demonstrate the effectiveness of the CK-SS across various scenarios.