An approach of multi-objective computing task offloading scheduling based NSGS for IOV in 5G

As a new technology, Internet of Vehicles (IoV) needs high bandwidth and low delay. However, the current on-board mobile terminal equipment cannot meet the needs of the IoV. Therefore, using mobile edge computing (MEC) can solve the problems of energy consumption and time delay in the IoV. In the MEC, task offloading can solve the problem of resource constraint on mobile devices effectively, but it is not optimal to offload all tasks to edge servers. In this paper, the vehicle computation task is regarded as a directed acyclic graph (DAG), and task nodes’ execution location and scheduling order are optimized. Considering the energy consumption and delay of the system, the vehicle computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then a Non-dominated Sorting Genetic Strategy(NSGS) is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay and energy consumption. Finally, a large number of experiments are carried to prove the performance of the algorithm. The experimental results show that the algorithm can make the optimal decision in practical applications.