Towards VM Consolidation Using a Hierarchy of Idle States

R. Singh, Tim Brecht, S. Keshav
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引用次数: 9

Abstract

Typical VM consolidation approaches re-pack VMs into fewer physical machines, resulting in energy and cost savings [13, 19, 23, 40]. Recent work has explored a just-in time approach to VM consolidation by transitioning VMsto an inactive state when idle and activating them on the arrival of client requests[17, 21]. This leads to increased VM density at the cost of an increase in client request latency (called miss penalty). The VM density so obtained, although greater, is still limited by the number of VMs that can be hosted in the one inactive state. If idle VMs were hosted in multiple inactive states, VM density can be increased further while ensuring small miss penalties. However, VMs in different inactive states have different capacities, activation times, and resource requirements. Therefore, a key question is: How should VMs be transitioned between different states to minimize the expected miss penalty? This paper explores the hosting of idle VMs in a hierarchy of multiple such inactive states, and studies the effect of different idle VMmanagement policies on VMdensity and miss penalties. We formulate a mathematical model for the problem, and provide a theoretical lower bound on the miss penalty. Using an off-the-shelf virtualization solution (LXC [2]), we demonstrate how the required model parameters can be obtained. We evaluate a variety of policies and quantify their miss penalties for different VM densities. We observe that some policies consolidate up to 550 VMs per machine with average miss penalties smaller than 1 ms.
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使用空闲状态层次结构实现VM整合
典型的虚拟机整合方法是将虚拟机重新打包到更少的物理机器中,从而节省能源和成本[13,19,23,40]。最近的工作探索了一种及时的VM整合方法,方法是在空闲时将VM转换为非活动状态,并在客户端请求到达时激活它们[17,21]。这会导致VM密度的增加,代价是客户端请求延迟的增加(称为miss penalty)。这样获得的虚拟机密度虽然更高,但仍然受到可以在一个非活动状态下托管的虚拟机数量的限制。如果空闲的虚拟机驻留在多个非活动状态,则可以进一步增加虚拟机密度,同时确保较小的错过惩罚。不同的未激活状态下,虚拟机的容量、激活次数和资源需求不同。因此,一个关键的问题是:vm应该如何在不同的状态之间转换,以最小化预期的错过惩罚?本文探讨了处于多个非活动状态的空闲虚拟机的托管问题,并研究了不同空闲虚拟机管理策略对虚拟机密度和缺失惩罚的影响。我们建立了该问题的数学模型,并给出了脱靶惩罚的理论下界。使用现成的虚拟化解决方案(LXC[2]),我们将演示如何获得所需的模型参数。我们评估了各种策略,并量化了不同VM密度下的失误惩罚。我们观察到,一些策略将每台机器合并到550个vm,平均缺失惩罚小于1 ms。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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