LAUSR

Vehicular Edge Computing (VEC) brings the computational resources in close proximity to the service requestors and thus supports explosive computing demands from smart vehicles. However, the limited computing capability of VEC cannot simultaneously respond to large amounts of offloading requests, thus restricting the performance of VEC system. Besides, a mass of traffic data can incur tremendous pressure on the front-haul links between vehicles and the edge server. To strengthen the performance of VEC, in this paper we propose to place services beforehand at the edge server, e.g., by deploying the services/tasks-oriented data (e.g., related libraries and databases) in advance at the network edge, instead of downloading them from the remote data center or offloading them from vehicles during the runtime. In this paper, we formulate the service placement problem in VEC to minimize the average response latency for all requested services along the slotted timeline. Specifically, the time slot spanned optimization problem is converted into per-slot optimization problems based on the Lyapunov optimization. Then a greedy heuristic is introduced to the drift-plus-penalty-based algorithm for seeking the approximate solution. The simulation results reveal its advantages over others in terms of optimal values and our strategy can satisfy the long-term energy constraint.