Joint Optimization of Offloading and Resource Allocation for SDN-Enabled IoV

Abstract

With the development of various vehicle applications, such as vehicle social networking, pattern recognition, and augmented reality, diverse and complex tasks have to be executed by the vehicle terminals. To extend the computing capability, the nearest roadside-unit (RSU) is used to offload the tasks. Nevertheless, for intensive tasks, excessive load not only leads to poor communication links but also results to ultrahigh latency and computational delay. To overcome these problems, this paper proposes a joint optimization approach on offloading and resource allocation for Internet of Vehicles (IoV). Specifically, assuming particle tasks assigned for vehicles in the system model are offloaded to RSUs and executed in parallel. Moreover, the software-defined networking (SDN) assisted routing and controlling protocol is introduced to divide the IoV system into two independent layers: data transmission layer and control layer. A joint approach optimized offloading decision, offloading ratio, and resource allocation (ODRR) is proposed to minimize system average delay, on the premise of satisfying the demand of the quality of service (QoS). By comparing with conventional offloading strategies, the proposed approach is proved to be optimal and effective for SDN-enabled IoV.