Mobile Edge Computing Offloading Strategy Under Internet of Vehicles Scenario

doi:10.19678/j.issn.1000-3428.0056850

Abstract

Abstract: In computing offloading systems of Mobile Edge Computing(MEC) in Internet of Vehicles(IoV),the loads of concurrent multi-priority computing tasks and MEC server resources are not balanced.To address the problem,this paper proposes a Genetic Algorithm-based Offloading Strategy(GAOS).The strategy sets weights of computing tasks with different priorities based on vehicle speed,MEC coverage and computing task features.On this basis,the computing tasks are coded.The problem of energy consumption minimization of computing offloading is transformed into a knapsack problem,and the optimal offloading strategy is obtained by using the genetic algorithm.Simulation results show that compared with the Random and all-MEC strategies,GAOS is least affected by the unbalanced loads of MEC servers,and increases the number of successfully processed on-board secure computing tasks by about 30% and 50% respectively.

Key words: Mobile Edge Computing(MEC), Internet of Vehicles(IoV), computing offloading, genetic algorithm, on-board computing task

摘要： 针对移动边缘计算（MEC）车联网计算卸载系统，考虑并发多个多优先级计算任务以及MEC服务器资源负载不均的情况，提出基于遗传算法的卸载策略GAOS。根据车辆速度、MEC覆盖情况以及计算任务特性，为不同优先级的计算任务设置权重。在此基础上，对车载计算任务进行编码，将优化问题转化为背包问题，并通过遗传算法求解得到最佳卸载策略。仿真结果表明，与Random和ALL-MEC策略相比，GAOS受MEC服务器负载不均的影响较小，对车载安全型计算任务的成功处理数量分别增加约30%和50%。

关键词: 移动边缘计算, 车联网, 计算卸载, 遗传算法, 车载计算任务

CLC Number:

TP391

YU Xiang, LIU Yixun, SHI Xueqin, WANG Zheng. Mobile Edge Computing Offloading Strategy Under Internet of Vehicles Scenario[J]. Computer Engineering, 2020, 46(11): 29-34,41.

余翔, 刘一勋, 石雪琴, 王政. 车联网场景下的移动边缘计算卸载策略[J]. 计算机工程, 2020, 46(11): 29-34,41.

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