整合环境中Xen虚拟机的内存超预定和动态控制

Jin Heo, Xiaoyun Zhu, Pradeep Padala, Zhikui Wang
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引用次数: 110

摘要

新出现的云计算环境在共享资源池上托管数百到数千个服务。虚拟化技术允许多个服务在单个物理节点上的不同虚拟机(vm)中运行,从而增强了共享功能。资源超额预订允许合并更多具有时变需求的服务,从而降低运营成本。在过去,研究人员已经研究了当CPU相对于所有vm的峰值需求总和超额时,如何动态控制CPU分配给虚拟机的机制。然而,除了在VMware平台上,还没有对多个vm之间的运行时内存重新分配进行广泛的研究。在本文中,我们提出了一个案例研究,其中反馈控制用于在统一环境中为Xen虚拟机分配动态内存。我们将说明内存在与应用程序级性能(如响应时间)的关系方面与CPU的不同之处。我们建立了一个联合资源控制系统的原型,用于实时分配CPU和内存资源给共置虚拟机。实验结果表明,我们的解决方案允许所有托管应用程序实现所需的性能,尽管它们的CPU和内存需求随时间变化,而没有内存控制的解决方案会导致严重的服务水平违规。
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Memory overbooking and dynamic control of Xen virtual machines in consolidated environments
The newly emergent cloud computing environments host hundreds to thousands of services on a shared resource pool. The sharing is enhanced by virtualization technologies allowing multiple services to run in different virtual machines (VMs) on a single physical node. Resource overbooking allows more services with time-varying demands to be consolidated reducing operational costs. In the past, researchers have studied dynamic control mechanisms for allocating CPU to virtual machines, when CPU is overbooked with respect to the sum of the peak demands from all the VMs. However, runtime re-allocation of memory among multiple VMs has not been widely studied, except on VMware platforms. In this paper, we present a case study where feedback control is used for dynamic memory allocation to Xen virtual machines in a consolidated environment. We illustrate how memory behaves differently from CPU in terms of its relationship to application-level performance, such as response times. We have built a prototype of a joint resource control system for allocating both CPU and memory resources to co-located VMs in real time. Experimental results show that our solution allows all the hosted applications to achieve the desired performance in spite of their time-varying CPU and memory demands, whereas a solution without memory control incurs significant service level violations.
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