计算机科学 ›› 2015, Vol. 42 ›› Issue (4): 206-208.doi: 10.11896/j.issn.1002-137X.2015.04.041

• 人工智能 • 上一篇    下一篇

基于社会力群智能优化算法的云计算资源调度

袁 浩,李昌兵   

  1. 重庆邮电大学电子商务与现代物流实验室 重庆400065,重庆邮电大学电子商务与现代物流实验室 重庆400065
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61003256),重庆邮电大学自然科学基金(A2011-21)资助

Resource Scheduling Algorithm Based on Social Force Swarm Optimization Algorithm in Cloud Computing

YUAN Hao and LI Chang-bing   

  • Online:2018-11-14 Published:2018-11-14

摘要: 为了提高云计算资源的调度效率,提出了一种基于社会力群智能优化算法的云计算资源调度方法。首先将云计算资源调度任务完成时间最短作为社会力群智能优化算法的目标函数,然后通过模拟人群疏散过程中的自组织、拥挤退避行为对最优调度方案进行搜索,最后采用仿真实验对算法性能进行测试。结果表明,相对于其它云计算资源调度方法,该方法可以更快地找到最优云计算资源调度方案,使云计算资源负载更加均衡,提高了云计算资源的利用率。

关键词: 云计算,社会力模型,资源调度,负载均衡

Abstract: In order to improve the resource scheduling efficiency in cloud computing,this paper put forward a cloud computing resource scheduling method based on social force swarm optimization algorithm.Firstly,resource scheduling task completion time was taken as objective function of swarm optimization algorithm based on social force model.And then the optimal scheduling scheme was found by simulation of crowd evacuation process of self-organization phenomenaand congestion avoidance behavior.Finally,the simulation experiments were carried out to test the performance of the proposed method.The results show that compared with other the resource scheduling method in cloud computing,the proposed method can quickly find the optimal resource scheduling scheme,make resource load more balanced,and improve the utilization rate of cloud computing resources.

Key words: Cloud computing,Social force model,Resource scheduling,Load balancing

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