基于自适应过载阈值选择的虚拟机动态整合方法

针对云环境下动态工作负载的不确定性，提出了基于自适应过载阈值选择的虚拟机动态整合方法。为了权衡数据中心能源有效性与服务质量间的关系，将自适应过载阈值的选择问题建模为马尔可夫决策过程，计算过载阈值的最优选择策略，并根据系统能效和服务质量调整阈值。通过过载阈值检测过载物理主机，然后根据最小迁移时间原则以及最小能耗增加放置原则确定虚拟机的迁移策略，最后切换轻负载物理主机至休眠状态完成虚拟机整合。仿真实验结果表明，所提出的方法在减少虚拟机迁移次数方面效果显著，在节约数据中心能源开销与保证服务质量方面表现良好，在能源的有效性与云服务质量二者之间取得了比较理想的平衡。

Virtual machine dynamic consolidation method based on adaptive overloaded threshold selection

Considering the uncertainty of dynamic workloads in cloud computing, an Virtual Machine (VM) dynamic consolidation method based on adaptive overloaded threshold selection was proposed. In order to make a trade-off between energy efficiency and Quantity of Services (QoS) of data centers, an adaptive overloaded threshold selection problem model based on Markov decision processes was designed. The optimal decision was calculated by solving this problem model, and the overloaded threshold was dynamically adjusted by using the optimal decision according to energy efficiency and QoS of data center. Overloaded threshold was used to predict overloaded hosts and trigger VM migrations. According to the principle of minimum migration time and minimum energy consumption growth, the VM migration strategy under overloaded threshold constraint was given, and the underloaded hosts were switched to sleep mode. Simulation results show that this method can significantly avoid excessive virtual machine migrations and decrease the energy consumption while improving QoS effectively; in addition, it can achieve an ideal balance between QoS and energy consumption of data center.