摘要
煤矿安全预警及决策任务部署在云端已成为一种趋势,云资源和监测预警任务的动态异构性,导致任务调度是云计算系统中的一个急需解决的问题。煤矿安全预警决策中需要实现最小化任务执行时间,提出一种基于种群分类的粒子群任务调度算法,提高算法的收敛精度,并建立煤矿云平台的任务调度模型,以优化任务的执行时间。实验结果表明,该算法在解决任务调度过程中与传统粒子群算法相比将执行时间提高7.13%,从而提升煤矿安全预警能力。
It has become a trend to deploy coal mine safety early warning and decision-making tasks in cloud.Due to the dynamic heterogeneity of cloud resources and monitoring and early warning tasks,task scheduling is an urgent problem in cloud computing system.A particle swarm optimization task scheduling algorithm based on population classification was proposed to improve the convergence accuracy of the algorithm,and a task scheduling model of coal mine cloud platform was established to optimize the task execution time.Experimental results show that the algorithm can improve the execution time by 7.13% compared with the traditional particle swarm optimization algorithm in the task scheduling process,thus improving the coal mine safety warning ability.
作者
姚军
马宇静
甄梓越
YAO Jun;MA Yujing;ZHEN Ziyue(College of Communication and Information Engineering,Xi'an University of Science and Technology,Xi'an 710600,China)
出处
《煤炭技术》
CAS
北大核心
2022年第5期145-148,共4页
Coal Technology
关键词
煤矿安全
安全预警
云计算
任务调度
粒子群优化算法
coal mine safety
safety warning
cloud environment
task scheduling
particle swarm optimization
