使用基于pmc的功率建模技术实现共享虚拟化环境的准确能源核算

Ramon Bertran Monfort, Y. Becerra, David Carrera, Vicencc Beltran, Marc González, X. Martorell, J. Torres, E. Ayguadé
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引用次数: 39

摘要

虚拟化基础设施提供商需要新的方法来提高用于向客户收费的会计模型的准确性。未来的数据中心将由多个核心系统组成,每个核心系统将承载大量虚拟机。虽然可以使用现有的系统工具实现资源利用率核算,但是当以每个vm粒度为目标时,能源核算是一项复杂的任务。在本文中,我们提出了一种方法,通过在每vm测量上增加前所未有的精确度,为能源会计带来了新的机会。我们提出了一个系统——它利用基于性能监控计数器(pmc)的CPU和内存功率模型——在虚拟化系统中执行能源会计。本文的贡献是双重的。首先,我们展示了基于pmc的功率建模方法在虚拟化环境中仍然有效。其次,我们介绍了一种计算虚拟化系统能耗的新方法。总体而言,英特尔®酷睿™2双核的结果显示,能量估计的误差低于5%。这种方法为服务和基础设施提供者使用的退款模型带来了灵活性。例如,我们展示了在相同时间内执行的vm,即使只考虑CPU和内存的消耗,在能耗方面也存在超过20%的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accurate energy accounting for shared virtualized environments using PMC-based power modeling techniques
Virtualized infrastructure providers demand new methods to increase the accuracy of the accounting models used to charge their customers. Future data centers will be composed of many-core systems that will host a large number of virtual machines (VMs) each. While resource utilization accounting can be achieved with existing system tools, energy accounting is a complex task when per-VM granularity is the goal. In this paper, we propose a methodology that brings new opportunities to energy accounting by adding an unprecedented degree of accuracy on the per-VM measurements. We present a system -which leverages CPU and memory power models based in performance monitoring counters (PMCs)- to perform energy accounting in virtualized systems. The contribution of this paper is twofold. First, we show that PMC-based power modeling methods are still valid on virtualized environments. And second, we introduce a novel methodology for accounting of energy consumption in virtualized systems. In overall, the results for an Intel® Core™ 2 Duo show errors in energy estimations below the 5%. Such approach brings flexibility to the chargeback models used by service and infrastructure providers. For instance, we show that VMs executed during the same amount of time, present more than 20% differences in energy consumption even only taking into account the consumption of the CPU and the memory.
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