摘要
智能车辆上的时延敏感型任务对计算能力的要求很高,然而请求车辆上可用的计算资源有限不足以单独处理整个任务数据,很难满足时延需求。车辆雾计算(Vehicle Fog Computing,VFC)通过在请求车辆附近进行计算卸载来改善车辆服务。文中基于两阶段生产计划对计算卸载过程进行建模,提出了一种计算卸载算法(Computation Offloading Algorithm,COA)来优化卸载决策和执行顺序,从而降低计算卸载时延。COA在遗传算法(Genetic Algorithm,GA)的基础上应用了Johnson Rules决定卸载顺序。通过SUMO和MATLAB仿真,显示出与GA相比,在相同的迭代次数下,COA具有更低的平均卸载时延和更好的稳定性。
Delay⁃sensitive tasks on intelligent vehicles require a high⁃level computing power.However,the computing resources available on the requested vehicles are not enough to process the entire task data alone,and can hardly meet the delay requirements.Vehicle fog computing(VFC)can improve vehicle service by offloading computation near the requesting vehicle.In this paper,the computation offloading process is modeled based on two⁃stage production planning,and a computation offloading algorithm(COA)is proposed to optimize the offloading scheme and execution sequence,thereby reducing the computation offloading delay.Based on the genetic algorithm(GA),COA applies Johnson Rules to determine the offloading order.Simulations on SUMO and MATLAB demonstrate that COA has lower average offloading latency and better stability than GA under the same number of iterations.
作者
鲍楠
周思瑶
孙希霞
左加阔
潘甦
BAO Nan;ZHOU Siyao;SUN Xixia;ZUO Jiakuo;PAN Su(School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《南京邮电大学学报（自然科学版）》
北大核心
2023年第5期16-22,共7页
Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition
基金
国家自然科学基金(62071244)
中国博士后科学基金(2021M693699)资助项目。
