[1]胡荣辉,王瑞通. 云环境下虚拟机集群迁移策略研究[J].计算机技术与发展,2017,27(11):33-36.
　HU Rong-hui,WANG Rui-tong. Study on Virtual Cluster Migration Strategy in Cloud Environment[J].,2017,27(11):33-36.
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 云环境下虚拟机集群迁移策略研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
33-36
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 Study on Virtual Cluster Migration Strategy in Cloud Environment
文章编号:
1673-629X（2017)11-0033-04
作者:
 胡荣辉; 王瑞通
 南京邮电大学 计算机学院
Author(s):
 HU Rong-hui; WANG Rui-tong
关键词:
 ; 数据中心; 虚拟机迁移; 资源匹配; 相关性
Keywords:
 ; data center; virtual machine migration; resource matching; relevance
分类号:
TP301.6
文献标志码:
A
摘要:
 针对因目前数据中心资源利用率偏低以及负载不平衡而导致数据中心能耗浪费严重的问题,提出了一种基于双门限与资源需求相关性匹配的虚拟机迁移算法.该算法采用双门限值方式触发虚拟机迁移策略,通过双门限触发的策略减少虚拟机迁移次数,从而避免系统频繁的虚拟机迁移.根据各虚拟机的资源利用率、带宽内存比以及与服务器资源的相关性进行待迁移虚拟机的选择,使得数据中心在降低迁移开销以及保证服务质量的同时,能够释放更多的服务器资源,并能通过迁移数量以及资源匹配程度确定待迁移服务器.仿真结果表明,提出的算法能有效降低虚拟机迁移成本,提高系统资源利用率,并在降低SLA违约方面有一定的改善,也避免了因多数虚拟机迁移到同一台服务器所形成的群聚效应.
Abstract:
 In allusion of problem of wasted energy consumption in the data center caused by its low resource utilization and imbalanced load,a virtual machine migration algorithm based on dual threshold and resource demand for relevance matching is proposed,which a-dopts two-threshold method to trigger the virtual machine migration strategy and avoids frequent VM migration by reducing the number of virtual machine migrations by the double-threshold triggered strategy. According to the resource utilization,bandwidth memory ratio and the relevance with server resource for each virtual machine,the data center has reduced the migration cost and guarantee the quality of service,which it can release more server resources and can determine the server to be migrated by the quantity of migration and the degree of resource matching meanwhile. The simulation results show that it has effectively reduced cost of virtual machine migration and promo-ted system resource utilization,with certain improvement in reducing the SLA breach,avoiding most virtual machine migration to the clus-ter effect formed by the same server.

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更新日期/Last Update: 2017-12-25