容器云环境虚拟资源配置策略的优化

针对容器化云环境中数据中心能耗较高的问题,提出了一种基于最佳能耗优先（Power Full,PF）物理机选择算法的虚拟资源配置策略。首先,提出容器云虚拟资源的配置和迁移方案,发现物理机选择策略对数据中心能耗有重要影响;其次,通过研究主机利用率与容器利用率,主机利用率与虚拟机利用率,主机利用率与数据中心能耗之间的数学关系,建立容器云数据中心能耗的数学模型,定义出优化目标函数;最后,通过对物理机的能耗函数使用线性插值进行模拟,依据邻近事物相类似的特性,提出改进的最佳能耗优先物理机选择算法。仿真实验将此算法与先来先得（First Fit,FF）、最低利用率优先（Least Fit,LF）、最高利用率优先（Most Full,MF）进行比较,实验结果表明,在有规律不同物理机群的计算服务中,其能耗比FF、LF、MF分别平均降低45%、53%和49%;在有规律相同物理机群的计算服务中,其能耗比FF、LF、MF分别平均降低56%、46%和58%;在无规律不同物理机群的计算服务中,其能耗比FF、LF、MF分别平均降低32%、24%和12%。所提算法实现了对容器云虚拟资源的合理配置,且在数据中心节能方面具有优越性。

Optimization of virtual resource deployment strategy in container cloud

Aiming at high energy consumption of data center in container cloud, a virtual resource deployment strategy based on host selection algorithm with Power Full (PF) was proposed. Firstly, the allocation and migration scheme of virtual resource in container cloud was proposed and the significant impact of host selection strategy on energy consumption of data center was found. Secondly, by studying the mathematical relationship between the utilization of host and the utilization of containers, between the utilization of host and the utilization of virtual machines and between the utilization of host and energy consumption of data center, a mathematical model of the energy consumption of data center in container cloud was constructed and an optimization objective function was defined. Finally, the function of host's energy consumption was simulated using linear interpolation method, and a host selection algorithm with PF was proposed according to the clustering property of the objects. Simulation results show that compared with First Fit (FF), Least Full (LF) and Most Full (MF), the energy consumption of the proposed algorithm is averagely reduced by 45%,53% and 49% respectively in the computing service of regular tasks and different host clusters; is averagely reduced by 56%,46% and 58% respectively in the computing service of regular tasks and same host cluster; is averagely reduced by 32%,24% and 12% respectively in the computing service of irregular tasks and different host clusters. The results indicate that the proposed algorithm realizes reasonable virtual resource deployment in container cloud, and has advantage in data center energy saving.