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[ "罗斯宁(1982- ),男,中国能源建设集团广西电力设计研究院有限公司高级工程师,主要研究方向为电力通信、调度自动化及信息化等" ]
[ "王化龙(1979- ),男,中国能源建设集团广西电力设计研究院有限公司高级工程师,主要研究方向为电力系统自动化、能源互联网运行控制系统及信息通信基础设施" ]
[ "李弘宇(1980- ), 男,中国能源建设集团广西电力设计研究院有限公司高级工程师,主要研究方向为电力通信网络规划" ]
[ "彭蔚(1995- ) 男,现就职于中国能源建设集团广西电力设计研究院有限公司,主要研究方向为电力通信工程" ]
网络出版日期:2020-02,
纸质出版日期:2020-02-20
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罗斯宁, 王化龙, 李弘宇, 等. 基于改进蚁群算法的云计算用户任务调度算法[J]. 电信科学, 2020,36(2):95-100.
Sining LUO, Hualong WANG, Hongyu LI, et al. Improved ant colony algorithm based cloud computing user task scheduling algorithm[J]. Telecommunications science, 2020, 36(2): 95-100.
罗斯宁, 王化龙, 李弘宇, 等. 基于改进蚁群算法的云计算用户任务调度算法[J]. 电信科学, 2020,36(2):95-100. DOI: 10.11959/j.issn.1000-0801.2020047.
Sining LUO, Hualong WANG, Hongyu LI, et al. Improved ant colony algorithm based cloud computing user task scheduling algorithm[J]. Telecommunications science, 2020, 36(2): 95-100. DOI: 10.11959/j.issn.1000-0801.2020047.
近年来,随着电力信息化的深入发展,越来越多的电力应用和任务在云端部署。由于云资源和电力应用的动态异构性,实现资源划分和任务调度是云计算系统中的一个挑战性问题。电力应用需要实现快速响应、实现最小完工时间,而调度程序又要考虑各个云计算节点的负载问题,保证云计算的可靠性。提出了一种基于改进蚁群算法的任务调度算法,解决虚拟机中的任务调度问题。通过对标准蚁群算法的改进,在最小化整体完工时间的同时实现任务调度时间的减小和负载均衡。研究结果表明,该算法有效缩短了任务调度时间,并实现云节点负载均衡,为电力云计算的优化提供技术依据。
In recent years
with the development of power information
more and more power applications and tasks are deployed in the cloud.Because of the dynamic heterogeneity of cloud resources and power applications
it is a challenge in the cloud computing system to realize resource division and task scheduling.Power applications need to be able to achieve a rapid response and minimum completion time
and schedulers should consider the load of each cloud computing node to ensure the reliability of cloud computing.A task scheduling algorithm based on the algorithm of improving an ant colony was proposed to solve the problem of task scheduling in virtual machines.Through the improvement of the standard ant colony algorithm
the task scheduling time was reduced and load balancing was realized while minimizing the overall completion time.The results show that the algorithm can shorten the task scheduling time and realize the load balancing of cloud nodes
which provides technical basis for the optimization of power cloud computing.
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王琳杰 . 云计算中基于生物共生机制改进粒子群优化的任务调度方案 [J ] . 电信科学 , 2016 , 32 ( 9 ): 113 - 119 .
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