Energy Aware Virtual Machine Scheduling in Data Centers

Power consumption is a primary concern in modern servers and data centers. Due to varying in workload types and intensities, different servers may have a different energy efficiency (EE) and energy proportionality (EP) even while having the same hardware configuration (i.e., central processing unit (CPU) generation and memory installation). For example, CPU frequency scaling and memory modules voltage scaling can significantly affect the server’s energy efficiency. In conventional virtualized data centers, the virtual machine (VM) scheduler packs VMs to servers until they saturate, without considering their energy efficiency and EP differences. In this paper we propose EASE, the Energy efficiency and proportionality Aware VM SchEduling framework containing data collection and scheduling algorithms. In the EASE framework, each server’s energy efficiency and EP characteristics are first identified by executing customized computing intensive, memory intensive, and hybrid benchmarks. Servers will be labelled and categorized with their affinity for different incoming requests according to their EP and EE characteristics. Then for each VM, EASE will undergo workload characterization procedure by tracing and monitoring their resource usage including CPU, memory, disk, and network and determine whether it is computing intensive, memory intensive, or a hybrid workload. Finally, EASE schedules VMs to servers by matching the VM’s workload type and the server’s EP and EE preference. The rationale of EASE is to schedule VMs to servers to keep them working around their peak energy efficiency point, i.e., the near optimal working range. When workload fluctuates, EASE re-schedules or migrates VMs to other servers to make sure that all the servers are running as near their optimal working range as they possibly can. The experimental results on real clusters show that EASE can save servers’ power consumption as much as 37.07%–49.98% in both homogeneous and heterogeneous clusters, while the average completion time of the computing intensive VMs increases only 0.31%–8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22%. The job completion time can be saved by 53.80%.

功耗是现代服务器和数据中心的首要关注点。由于工作负载类型和强度的不同，即使具有相同的硬件配置（即中央处理器（CPU）代数和内存安装），不同的服务器可能具有不同的能效（EE）和能量比例性（EP）。例如，CPU频率缩放和内存模块电压缩放会显著影响服务器的能效。在传统的虚拟化数据中心，虚拟机（VM）调度器将虚拟机分配到服务器直至其饱和，而不考虑它们的能效和能量比例性差异。在本文中，我们提出了EASE，即包含数据收集和调度算法的能效和能量比例性感知虚拟机调度框架。在EASE框架中，首先通过执行定制的计算密集型、内存密集型和混合基准测试来确定每个服务器的能效和能量比例性特征。服务器将根据其能量比例性和能效特征，按照其对不同传入请求的亲和性进行标记和分类。然后，对于每个虚拟机，EASE将通过跟踪和监测其包括CPU、内存、磁盘和网络在内的资源使用情况进行工作负载特征描述过程，并确定它是计算密集型、内存密集型还是混合工作负载。最后，EASE通过匹配虚拟机的工作负载类型以及服务器的能量比例性和能效偏好，将虚拟机调度到服务器。EASE的基本原理是将虚拟机调度到服务器，使其在接近其峰值能效点（即接近最优工作范围）工作。当工作负载波动时，EASE会重新调度或迁移虚拟机到其他服务器，以确保所有服务器都尽可能在接近其最优工作范围的状态下运行。在真实集群上的实验结果表明，在同构和异构集群中，EASE可节省服务器功耗多达37.07% - 49.98%，而计算密集型虚拟机的平均完成时间仅增加0.31% - 8.49%。在异构节点中，计算密集型虚拟机的功耗可降低44.22%，作业完成时间可节省53.80%。