云计算中基于多目标优化的动态资源配置方法

目前，云平台的大多数动态资源分配策略只考虑如何减少激活物理节点的数量来达到节能的目的，以实现绿色计算，但这些资源再配置方案很少考虑到虚拟机放置的稳定性。针对应用负载的动态变化特征，提出一种新的面向多虚拟机分布稳定性的基于多目标优化的动态资源配置方法，结合各应用负载的当前状态和未来的预测数据，综合考虑虚拟机重新放置的开销以及新虚拟机放置状态的稳定性，并设计了面向虚拟机分布稳定性的基于多目标优化的遗传算法（MOGANS）进行求解。仿真实验结果表明，相对于面向节能和多虚拟机重分布开销的遗传算法（GA-NN），MOGANS得到的虚拟机分布方式的稳定时间是GA-NN的10.42倍；同时，MOGANS也较好权衡了多虚拟机分布的稳定性和新旧状态转换所需的虚拟机迁移开销之间的关系。

Dynamic resource configuration based on multi-objective optimization in cloud computing

Currently, most resource reallocation methods in cloud computing mainly aim to how to reduce active physical nodes for green computing, however, node stability of virtual machine placement solution is not considered. According to varying workload information of applications, a new virtual machine placement method based on multi-objective optimization was proposed for node stability, considering both the overhead of virtual machine reallocation and the stability of new virtual machine placement, and a new Multi-Objective optimization based Genetic Algorithm for Node Stability (MOGANS) was designed to solve this problem. The simulation results show that, the stability time of Virtual Machine (VM) placement obtained by MOGANS is 10.42 times as long as that of VM placement got by GA-NN (Genetic Algorithm for greeN computing and Numbers of migration). Meanwhile, MOGANS can well balance stability time and migration overhead.