A novel multi-application offloading method formobile intelligent vehicles in a cloud-edge hybridenvironment

Abstract With the increasing demand for real-time computing services in the Internet ofVehicles(IoV) and the inability to meet the latency requirements of IoVapplications by using cloud computing, multi-access edge computing(MEC) isconsidered a promising paradigm to address the latency-sensitive andcomputation-intensive requirements for meeting IoV applications. We considerthe dependencies in IoV applications and design a hybrid cloud-edge architectureto ensure adaptability and flexibility. We propose a scheduling algorithm thatoffloads tasks to the eNode or cloud for execution after sorting them by urgency.Experimental results show that our algorithm outperforms existing algorithms interms of average completion time, average waiting time, and in-time completionrate of the application, which can provide a better quality of service(QoS) for IoV.