摘要
边缘计算任务卸载和资源调度是当前计算机科学与信息技术领域中备受关注的研究方向之一,是具有广阔前景的研究方向。目前,尚未有文献对边缘计算任务卸载和资源调度的方法在时延、能耗、负载、调度效率方面的性能进行全面的对比与归纳,并给出研究进展、挑战及发展方向。针对以上问题,首先回顾了边缘计算技术的发展脉络;其次总结了边缘计算调度框架,并将时延、能耗、资源负载均衡和调度效率作为评价算法性能的指标进行计算建模,并根据相应指标的优化构建问题模型;然后,将边缘计算调度方法分为经典方法和进阶方法进行对比分析;接着,调研车联网与自动驾驶、虚拟现实与云游戏、智慧城市与智能交通、工业物联网与智能制造、智慧生活与医疗边缘的应用研究现状;最后,给出目前存在的挑战和展望,以期为研究者提供理论指导和借鉴。
Edge computing task offloading and resource scheduling is one of the current research directions that have attracted much attention in the field of computer science and information technology,and it is a promising research direction.At present,no literature has been published to comprehensively compare and summarize the performance of edge computing task offloading and resource scheduling methods in terms of delay,energy consumption,load and scheduling efficiency,and to give the research progress,challenges and development directions.To address the above issues,the development of edge computing technology was reviewed;the edge computing scheduling framework was summarized,and the delay,energy consumption,resource load balancing and scheduling efficiency were taken as the indicators for evaluating the performance of algorithms to model computationally,and the problem model was constructed based on the optimization of the corresponding indexes.Then,edge computing scheduling methods were classified into classical and advanced methods for comparative analysis;the application status of vehicle networking and autonomous driving,virtual reality and cloud gaming,smart city and intelligent transportation,industrial internet of things and intelligent manufacturing,and smart life and medical edge were investigated.The challenges and prospects were given to provide theoretical guidance and reference for researchers.
作者
周绪
苗辉
杨静
江武
廖晓燕
李逸骏
李少波
鲁加林
ZHOU Xu;MIAO Hui;YANG Jing;JIANG Wu;LIAO Xiaoyan;LI Yijun;LI Shaobo;LU Jialin(State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;BaishanCloud,Guiyang 550025,China)
出处
《计算机集成制造系统》
北大核心
2025年第8期2695-2726,共32页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(62166005)
国家重点研发计划资助项目(2018AAA0101800)
教育部重点实验室开放基金资助项目(黔教合KY字[2020]248)
贵州省自然科学基金资助项目(黔科合基础-ZK[2022]一般130)。
关键词
边缘计算
资源调度
任务卸载
性能建模
调度方法
edge computing
resource scheduling
task offloading
performance modeling
scheduling methods
