Research on Multi-task Partial Offloading Scheme in Vehicular Edge Computing

Nowadays, the existing vehicular applications have more stringent requirements for delay. Vehicular Edge Computing (VEC) is able to take advantage of network edges devices, such as Road Side Unit (RSU), for collaborative processing, which can effectively reduce the latency. Most existing studies assume that RSU has the sufficient computing resources to provide the unlimited services. But in fact, its computing resources will be limited with the increase of the number of processing tasks, which will restrict the delay sensitive vehicular applications. To solve this problem, a multi-task partial offloading scheme in vehicular edge computing is proposed in this paper. To minimize the total task processing delay, the remaining available computing resources of adjacent vehicles is considered under the condition of making full use of RSU computing resources in this scheme. Firstly, under the constrains of delay and resource, the optimal offloading decision variable ratio of local, RSU and adjacent vehicle for each task are allocated. Secondly, in order to minimize processing delay, the appropriate spare vehicle is selected in one-hop range as adjacent vehicles to process part of the task. Simulation results show the scheme proposed can reduce the delay better compared with other schemes.