Efficient resource provisioning in compute clouds via VM multiplexing
Efficient resource provisioning in compute clouds via VM multiplexing
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DOI：
10.1145/1809049.1809052
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发表时间：
2010-06
影响因子：
8.6
通讯作者：
Xiaoqiao Meng;C. Isci;J. Kephart;Li Zhang;E. Bouillet;Dimitrios E. Pendarakis
中科院分区：
文献类型：
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作者：
Xiaoqiao Meng;C. Isci;J. Kephart;Li Zhang;E. Bouillet;Dimitrios E. Pendarakis
计算云中的资源配置通常需要估计虚拟机 (VM) 的容量需求。估计的虚拟机大小是根据工作负载需求分配资源的基础。与单独估计虚拟机大小的传统做法相比，我们提出了一种联合虚拟机大小调整方法，其中根据对其总容量需求的估计来整合和配置多个虚拟机。这种新方法利用多个虚拟机的工作负载模式之间的统计复用，即一种工作负载模式中的峰值和谷值不一定与其他工作负载模式一致。因此，低利用率VM的未使用资源可以被引导至其他共置的具有高利用率的VM。与基于单独虚拟机的配置相比，联合虚拟机规模调整和配置可能会带来更高的资源利用率。本文提出了三个设计模块，以在实践中实现这一概念。具体来说，性能约束描述了虚拟机实现一定水平的应用程序性能所需的容量；用于估计联合配置虚拟机大小的算法；一种虚拟机选择方法，旨在找到可以一起配置的良好虚拟机组合。我们展示了所提出的三个模块可以无缝插入到现有应用程序中，例如资源配置，并为虚拟机提供资源保证。通过监控从商业数据中心约 16,000 个虚拟机收集的数据来评估所提出的算法和应用程序。这些评估显示总体资源利用率提高了 45% 以上。
Resource provisioning in compute clouds often require an estimate of the capacity needs of Virtual Machines (VMs). The estimated VM size is the basis for allocating resources commensurate with workload demand. In contrast to the traditional practice of estimating the VM sizes individually, we propose a joint-VM sizing approach in which multiple VMs are consolidated and provisioned, based on an estimate of their aggregate capacity needs. This new approach exploits statistical multiplexing among the workload patterns of multiple VMs, i.e., the peaks and valleys in one workload pattern do not necessarily coincide with the others. Thus, the unused resources of a low utilized VM can be directed to the other co-located VMs with high utilization. Compared to individual VM based provisioning, joint-VM sizing and provisioning may lead to much higher resource utilization. This paper presents three design modules to enable the concept in practice. Specifically, a performance constraint describing the capacity need of a VM for achieving a certain level of application performance; an algorithm for estimating the size of jointly provisioning VMs; a VM selection method that seeks to find good VM combinations for being provisioned together. We showcase that the proposed three modules can be seamlessly plugged into existing applications such as resource provisioning, and providing resource guarantees for VMs. The proposed algorithms and applications are evaluated by monitoring data collected from about 16 thousand VMs in commercial data centers. These evaluations reveal more than 45% improvements in terms of the overall resource utilization.