Research on task migration strategy based on discrete binary particle swarm optimization in edge computing environment

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Research on task migration strategy based on discrete binary particle swarm optimization in edge computing environment
	Wang C(王晨)2,3,4; Wang TY(王天宇)3,4; Zeng, Zeng 1; Yuan DC(袁德成)2; Jiang YH(蒋一恒)3,4
	2021
会议日期	October 22-24, 2021
会议地点	Beijing, China
关键词	edge computing Particle swarm optimization algorithm Task migration
页码	6439-6444
英文摘要	In view of the increasing number of edge servers in edge computing environment, how to make each server give full play to its own performance and improve the efficiency of task migration has become the focus of edge computing research. In this paper, an optimized discrete binary particle swarm optimization (DBPSO) algorithm is proposed for task migration. By optimizing the inertia weight and learning factors, the binary particle swarm optimization algorithm is compared with the random task distribution, and the optimal solution of task migration is obtained. Through the simulation test in cloudsim, the optimized algorithm can migrate tasks more efficiently. The effectiveness is reflected in the time complexity and energy consumption of migration.
产权排序	1
会议录	Proceeding - 2021 China Automation Congress, CAC 2021
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-6654-2647-3
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/30794]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Wang C(王晨)
作者单位	1.State Grid Jiangsu Electric Power Co., Ltd., Information and Telecommunication Branch, NanJing, China 2.Shenyang University of Chemical Technology, College of information Engineering, Shenyang, China 3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 4.Institutes for Robtics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	Wang C,Wang TY,Zeng, Zeng,et al. Research on task migration strategy based on discrete binary particle swarm optimization in edge computing environment[C]. 见:. Beijing, China. October 22-24, 2021.