私有云平台的虚拟机内存调度策略

在私有云平台中，现有的方法无法灵活地对虚拟机内存资源进行有效的监控和分配。针对以上问题，提出了内存实时监测和动态调度(MMS)模型，利用libvirt函数库和Xen提供的libxc函数库实现了对虚拟机内存紧缺、内存空闲时的实时监测和动态调度，并且提出虚拟机迁移策略，有效地缓解宿主机的内存紧缺问题。最后选取一台物理机作为主控节点，两台物理机作为子节点，利用Eucalyptus搭建一个小型的私有云平台。结果显示，当宿主机处于内存紧缺状态时，MMS系统通过启动虚拟机迁移策略有效地释放了内存空间；当虚拟机占用内存逼近初始最大内存时，MMS为其分配新的最大内存；当占用内容降低时，MMS系统对部分空闲的内存资源进行了回收，而且释放内存不超过150MB(最大内存512MB)时，其对虚拟机性能的影响不大。结果表明该模型对私有云平台中虚拟机内存进行实时监测和动态调度是有效的。

Memory scheduling strategy for virtual machine in private cloud platform

On the private cloud platform, it cannot be flexible to monitor and distribute the virtual machine memory resources effectively using the existing methods. To solve this problem, a Memory Monitor and Scheduler (MMS) model was put forward. And the real-time monitoring and dynamic scheduling of the virtual machine memory shortage and memory free were realized by using the libvirt function library and libxc function library provided by Xen. A small private cloud platform was built using Eucalyptus with regarding one physical machine as master node and two physical machines as child nodes. In the experiments, when the state of host was in memory shortage, MMS system effectively released the memory space by starting the virtual machine migration strategy; when the memory of the virtual machine was approaching the initial maximum memory, MMS system assigned it with a new maximum memory; when the occupied memory decreased, MMS system recycled part of free memory resource, which has little effect on the performance of virtual machines if the release memory did not exceed 150MB (maximum memory is 512MB). The results show that the MMS model of private cloud platform is effective for real-time monitoring and dynamic scheduling of the memory.