车联网中基于麻雀搜索算法的计算卸载策略  

Computing offloading strategy based on sparrow search algorithm in vehicular networks

作　　者：杨超 王宗山[1] 聂仁灿 丁洪伟[1,2] 李波[1] YANG Chao;WANG Zong-shan;NIE Ren-can;DING Hong-wei;LI Bo(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;University Key Laboratory of Internet of Things Technology and Application,Yunnan University,Kunming 650504,China)

机构地区：[1]云南大学信息学院,云南昆明650504 [2]云南大学云南省高校物联网技术及应用重点实验室,云南昆明650504

出　　处：《计算机工程与设计》2023年第1期1-7,共7页Computer Engineering and Design

基　　金：云南大学研究生科研创新基金项目(2020306);国家自然科学基金项目(61461053)。

摘　　要：针对车联网场景下的边缘计算系统中MEC服务器负载不均衡,紧急任务无法得到优先处理的问题,提出一种基于麻雀搜索算法的计算卸载策略(COSSA)。以最小化VEC系统的任务计算时延和MEC资源服务费为目标建立数学模型,利用层次分析法根据任务的属性为每个需要卸载任务分配优先级,运用麻雀搜索算法根据目标函数找出最优的卸载决策,实现服务器负载均衡。实验结果表明,与Random、ALP和OMP策略相比,COSSA策略可以有效地降低系统开销、均衡MEC服务器负载。Aiming at the problem that the MEC server load in the edge computing system in the internet of vehicles scene is unbalanced and urgent tasks cannot be processed first,a computing offloading strategy based on the sparrow search algorithm(COSSA)was proposed.The mathematical model was established with the goal of minimizing the task calculation delay of the VEC system and the MEC resource service fee.The analytic hierarchy process was used to assign priority to each task that needed to be offloaded according to the attributes of the task,and the sparrow search algorithm was used to find out the optimal offloading decision according to the objective function to achieve server load balancing.Experimental results show that,compared with Random,ALP,and OMP strategies,the COSSA strategy can effectively reduce system overhead and balance the load of MEC servers.

关 键 词：车联网 边缘计算 计算卸载 麻雀搜索算法 层次分析法 任务优先级分配 负载均衡 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]