基于软件定义网络和移动边缘计算的车联网高效任务卸载方案

随着车联网(IoV)中车辆和智能应用数目的增加使计算密集型任务激增，传统架构难以满足用户需求。为解决车联网计算资源不足且分配不均匀、应用时延需求无法满足、任务能耗成本较高的问题，结合移动边缘计算(MEC)和软件定义网络(SDN),设计了一种从宏基站到MEC服务器到车辆的车联网架构中的高效任务卸载方案，并提出一种改进的低复杂度非支配排序遗传算法，优化任务卸载成本和MEC服务器的负载均衡率。实验仿真结果表明，相比于随机卸载，NO-MEC卸载，NO-I卸载，传统NSGA、NSGA-Ⅱ卸载，GA卸载，Q-learning卸载，DQN卸载方案，所提方案有着更低的卸载成本，更优的负载均衡率，得到近似最高的系统效用，能够给车联网中的车辆用户带来更优质的网络服务。

Efficient task offloading scheme of Internet of Vehicles based on software defined network and mobile edge computing

With the increase in the number of vehicles and intelligent applications in the IoV, computing intensive tasks have proliferated, and the traditional architecture is difficult to meet user needs. To solve the problems of insufficient and uneven allocation of computing resources in the IoV, insatiable application delay requirements, and high task energy consumption costs, this paper combined MEC and SDN to design an efficient task offloading scheme in the network of vehicles architecture from macro station to MEC server to vehicle, and proposed an improved low-complexity non-dominated sorting genetic algorithm to optimize the task offloading cost and the load balancing rate of MEC server. The experimental simulation results show that the proposed scheme compared with random offloading, NO-MEC offloading, NO-I offloading, traditional NSGA, NSGA-II offloading, GA offloading, Q-learning offloading, DQN offloading, has lower offloading cost, better load balancing rate, and approximately the highest system utility, which brings better network services to vehicle users in the IoV.