Improved ant colony algorithm based cloud computing user task scheduling algorithm

Sining LUO; Hualong WANG; Hongyu LI; Wei PENG

doi:10.11959/j.issn.1000-0801.2020047

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Improved ant colony algorithm based cloud computing user task scheduling algorithm

Power Informationization Column | 更新时间：2024-06-05

- Improved ant colony algorithm based cloud computing user task scheduling algorithm
- Telecommunications Science Vol. 36, Issue 2, Pages: 95-100(2020)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2020047
  CLC： TP393
- Published Online：2020-02，
  
  Published：20 February 2020
- 稿件说明：
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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：

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.

摘要

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。

Abstract

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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references

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