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云计算资源调度问题求解的布谷鸟搜索算法
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  • 英文篇名:Cuckoo Search Algorithm for Solving Problem of Cloud Computing Resource Scheduling
  • 作者:李佳 ; 夏云霓
  • 英文作者:LI Jia;XIA Yun-ni;Academy of Innovation Education,Chongqing Radio and TV University;College of Computer Science,Chongqing University;
  • 关键词:云计算系统 ; 资源利用率 ; 资源调度 ; 布谷鸟搜索算法
  • 英文关键词:Cloud computing system;;resource utilization rate;;resource scheduling;;cuckoo search algorithm
  • 中文刊名:JZDF
  • 英文刊名:Control Engineering of China
  • 机构:重庆广播电视大学创新教育学院;重庆大学计算机学院;
  • 出版日期:2019-01-20
  • 出版单位:控制工程
  • 年:2019
  • 期:v.26;No.169
  • 基金:国家自然科学基金面上项目(NSF61472051);; 重庆市科委前沿与应用基础研究项目(cstc2014jcyjA40010);; 重庆广播电视大学科研项目(YB2016-17)
  • 语种:中文;
  • 页:JZDF201901029
  • 页数:5
  • CN:01
  • ISSN:21-1476/TP
  • 分类号:172-176
摘要
资源调度直接决定云计算系统的性能,是当前研究的热点,针对当前算法存在的执行时间长、计算复杂度高等不足,以提高云计算资源的利用率,提出了布谷鸟搜索算法的云计算资源调度策略。首先对云计算资源调度问题进行分析,采用安全强度和用户需求对虚拟机和安全需求的等级进行评价,然后构建云计算资源调度问题的数学模型,引入布谷鸟搜索算法求解到云计算资源调度数学模型的解,最后在CloudSim平台上对云计算资源调度模型的性能进行了分析。实验结果表明,布谷鸟搜索算法能够快速找到云计算资源调度的最佳方案,可以满足用户的实际要求,且结果好于对比模型。
        Resource scheduling is a hot topic in cloud computing research,in view of problems of long execution time and high computational complexity of the current algorithm and to improve the utilization rate of cloud computing resources,a cuckoo search algorithm for cloud computing resource scheduling model is proposed in this paper.Firstly,the cloud computing resource scheduling problem is analyzed,the level of the virtual machine and security requirements are evaluated by using security strength and user's requirements.Secondly,a mathematical model of the cloud computing resource scheduling problem is constructed,and the cuckoo search algorithm is introduced to solve the mathematical model of cloud computing resource scheduling.Finally,the performance of the cloud computing resource scheduling model is analyzed on the CloudSim platform.The results show that the proposed algorithm can quickly find the scheduling scheme of cloud computing resources and can satisfy the actual request of users,and the result is better than the contrast models.
引文
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