云数据中心基于贪心模式的虚拟机选择算法

针对如何从云数据中心的异常物理主机中选择出候选迁移虚拟机列表是虚拟机迁移中的问题,提出了基于贪心模式的虚拟机选择算法(GAO-VMS)。GAO-VMS每次都选择那些目标函数最优的虚拟机作为标准来迁移,形成候选迁移虚拟机列表,它有三类贪心模式:最大能量降低消耗策略(MPR)、最小迁移时间及能量消耗均衡策略(TPT)、最小每秒百万条指令数虚拟机请求策略(VVM)。使用CloudSim模拟器作为GAO-VMS的仿真环境。仿真结果表明:与常见的虚拟机迁移策略相比较,GAO-VMS使得云数据中心的能量消耗减少了30%~35%,虚拟机迁移次数减少了40%~45%,服务等级协议(SLA)违规率以及SLA违规和能量消耗联合指标只有5%的增加。GAO-VMS策略可用于企业构造绿色云计算中心。

Greedy algorithm optimization based virtual machine selection strategy in cloud data center

In the virtual machine migration process,one of the most problems is how to select the candidate migrating virtual machine list from the abnormal physical hosts in cloud data center.Therefore,a Greedy Algorithm Optimization based Virtual Machine Selection algorithm(GAO-VMS)was proposed.In GAO-VMS,the virtual machines with the optimal objective functions would be selected to perform the migration and the candidate migration virtual machine list was formed subsequently.There are three kinds of greedy modes in GAO-VMS:Maximum Power Reduction Policy(MPR),minimum migration Time and Power Tradeoff policy(TPT)and Violated million instructions per second-Virtual Machines policy(VVM).GAO-VMS was evaluated on CloudSim simulator.Simulation results show that compared to the common virtual machine migration strategy,GAO-VMS reduces the energy consumption of cloud data center by 30%-35%,and reduces the number of virtual machine migrations by 40%-45%with 5%increment of the Service Level Agreement(SLA)violation rate and the joint index of SLA violation and energy.The proposed GAO-VMS strategy can be used for enterprises to construct green cloud computing center.