基于负载高峰特征的虚拟机放置算法

提出了一种基于虚拟机负载高峰特征的虚拟机放置策略,通过更好地复用物理主机资源来实现资源共享,从而提高资源利用率.在云环境下,当多个虚拟机的负载高峰出现在相同的时间段内时,非高峰时段的资源利用率就会明显偏低;相反,多个虚拟机只要负载高峰能错开在不同的时间,闲置的资源就能更充分地被利用.由于应用的负载通常具有一定的周期性,因此,可以利用虚拟机负载的历史数据作为分析的依据.基于虚拟机的负载高峰特征对虚拟机负载进行建模,建立虚拟机负载之间的相似度矩阵来实现虚拟机联合放置.使用CloudSim模拟实现了所提出的算法,并与基于相关系数的放置算法、随机放置算法进行了比较.实验结果表明:所提算法在平均CPU利用率上有8.9%~12.4%的提高,主机使用量有8.2%~11.0%的节省.

Virtual Machine Placement Algorithm Based on Peak Workload Characteristics

This paper proposes a novel method to allocate virtual machines by statistical resource multiplexing based on the characteristics of virtual machine''s peak workloads. In a cloud environment, if the peak workloads of multiple virtual machines overlap, the resource utilization of the cloud system can be significantly low when all these virtual machines enter the non-peak workload phases. If the overlap of workload peaks among virtual machines can be avoided, resource utilization will not fluctuate so much (heavily loaded during peak period and largely idle during non-peak period). Since the workload of an application usually follows a cyclic pattern, historical data can be analyzed to predict future workload. This paper models the workload characteristics of virtual machines through monitoring their peak workloads. A similarity matrix of VM''s workloads is used to allocate virtual machines so that their workload peaks will not overlap. The performance study using CloudSim demonstrates that the proposed virtual machine allocation algorithm improves the CPU utilization by 8.9% to 12.4% under different workloads compared to the random allocation algorithm. The number of hosts needed by the algorithm is also reduced by 8.2% to 11.0% under the same workload requirements.