A Parallel Intelligence-Driven Resource Scheduling Scheme for Digital Twins-Based Intelligent Vehicular Systems

Abstract

Real-time digital twin technology can enhance traffic safety of intelligent vehicular system and provide scientific strategies for intelligent traffic management. At the same time, real-time digital twin depends on strong computation from vehicle side to cloud side. Aiming at the problem of delay caused by the dual dependency of timing and data between computation tasks, and the problem of unbalanced load of mobile edge computing servers, a parallel intelligence-driven resource scheduling scheme for computation tasks with dual dependencies of timing and data in the intelligent vehicular systems (IVS) is proposed. First, the delay and energy consumption models of each computing platform are formulated by considering the dual dependence of sub-tasks. Then, based on the bidding idea of the auction algorithm, the allocation model of computing resources and communication resources is defined, and the load balance model of the mobile edge computing (MEC) server cluster is formulated according to the load status of each MEC server. Secondly, joint optimization problem for offloading, resource allocation, and load balance is formulated. Finally, an adaptive particle swarm with genetic algorithm is proposed to solve the optimization problem. The simulation results show that the proposed scheme can reduce the total cost of the system while satisfying the maximum tolerable delay, and effectively improve the load balance of the edge server cluster.