Cooperative Service Caching in Vehicular Edge Computing Networks Based on Transportation Correlation Analysis

In vehicular edge computing, the vehicular services are cached and replaced among roadside units (RSUs) to minimize delay in delay-sensitive services. However, without prior knowledge of these service preferences, the cached services suffer a low-hit ratio and a high-network delay. Before the appearance of requests from vehicles, most existing vehicular service caching methods design the service-sharing mechanisms among RSUs to optimize the hit ratio, but they ignore the influence of vehicle mobility on service-sharing. Moreover, in vehicular services replacement, the differences between actual and historical vehicle trajectories negatively impact the service-sharing mechanisms constructed before, which existing methods ignored. Thus, this article addresses the vehicular service caching problem based on the surrounding function-features of vehicles and transportation correlations. First, we use the surrounding function-features of vehicles to estimate the service preference and ensure the hit ratio of cached services. Second, we formulate the vehicular service caching problem as a constrained optimization problem and design a cooperation mechanism between RSUs, which is based on transportation correlations of vehicle trajectories. Third, we propose two vehicular service caching methods based on Gibbs sampling to optimize the network delay of vehicular services and deal with the negative influence of vehicle mobility. Simulation in real data sets from Shenzhen, China shows that our methods have a better network delay and hit ratio than existing baselines by 15.26% and 5.6%, respectively.