云计算环境下多工作流任务调度方法研究  

Research on Multi workflow Task Scheduling Method in Cloud Computing Environment

作　　者：曾雅丽 孙滨 ZENG Ya-li;SUN Bin(College of Computer Science&Engineering,Hunan University of Information Technology,Changsha Hunan 410100,China;College of Information Engineering,Zhengzhou University of Industrial Technology,Zhengzhou Henan 451150,China;College of Computer and Information Engineering,Henan Normal University,Xinxiang Henan 453000,China)

机构地区：[1]湖南信息学院计算机科学与工程学院,湖南长沙410100 [2]郑州工业应用技术学院信息工程学院,河南郑州451150 [3]河南师范大学计算机与信息工程学院,河南新乡453000

出　　处：《计算机仿真》2025年第1期271-274,394,共5页Computer Simulation

摘　　要：为了有效解决多工作流任务调度过程中存在能耗比较高和执行时间比较长等问题,提出一种云计算环境下多工作流任务调度方法。引入多目标优化概念,以最短调度时间、最少费用和最小能耗为目标,建立多工作流任务调度模型。将遗传算法和蚁群算法两者动态融合,采用动态融合遗传蚁群算法对模型求解,确定最佳任务调度方案。实验结果表明,采用所提方法可以有效降低能耗和工作流的完成时间,可以获取更加理想的节能效果,并且平均资源利用率较高,具有一定的实际应用价值。In order to effectively solve the problems of high energy consumption and long execution time in multiworkflow task scheduling,this paper presented a multi-workflow task scheduling method in a cloud computing environment.Firstly,the concept of multi-objective optimization was introduced.Secondly,a multi-workflow task scheduling model was built to achieve the shortest scheduling time,minimum cost,and minimum energy consumption.Then,a genetic algorithm was dynamically integrated with ant colony algorithm to solve the model.Finally,the optimal task scheduling scheme was determined.Experimental results show that the proposed method can effectively reduce energy consumption and the completion time of workflow,thus achieving more ideal energy-saving effects,with a high resource utilization rate.Therefore,this method has certain practical application value.

关 键 词：动态融合遗传蚁群算法 云计算 多工作流 任务调度 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]