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Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds

机译：节能云中基于性能/功率比的虚拟机分配和迁移

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摘要

The last decade witnessed a dramatic advance in cloud computing research and techniques. One of the key challenges in this field is reducing the massive amount of energy consumption in cloud computing data centers. Many power-aware virtual machine (VM) allocation and consolidation approaches were proposed to reduce energy consumption efficiently. However, most of the existing efficient cloud solutions save energy at the cost of significant performance degradation. In this paper, we propose a strategy to calculate the optimized working utilization levels for host computers. As the performance and power data need to be measured on real platforms, to make our design practical, we propose a strategy named "PPRGear'' which is based on the sampling of utilization levels with distinct Performance-to-Power Ratios (PPR) calculated as the number of Server Side Java operations completed during a certain time period divided by the average active power consumption in that period. In addition, we present a framework for virtual machine allocation and migration which leverages the PPR for various host types. By achieving the optimal balance between host utilization and energy consumption, our framework is able to ensure that host computers run at the most power-efficient utilization levels, i.e., the levels with the highest PPR, thus tremendously reducing energy consumption with ignorable sacrifice of performance. Our extensive experiments with real world traces show that compared with three baseline energy-efficient VM allocation and selection algorithms, IqrMc, MadMmt, and ThrRs, our framework is able to reduce the energy consumption up to 69.31% for various host computer types with fewer migration times, shutdown times, and little performance degradation for cloud computing data centers. (C) 2019 Elsevier B.V. All rights reserved.
机译：过去十年见证了云计算研究和技术的巨大进步。该领域的主要挑战之一是减少云计算数据中心的大量能耗。为了有效降低能耗，提出了许多具有功耗意识的虚拟机（VM）分配和整合方法。但是，大多数现有的高效云解决方案都以显着的性能下降为代价来节省能源。在本文中，我们提出了一种策略来计算主机的最佳工作利用率水平。由于需要在真实平台上测量性能和功率数据，为了使我们的设计切实可行，我们建议使用一种名为“ PPRGear”的策略，该策略基于对利用率水平的采样，并计算出不同的性能/功率比（PPR）通过在一定时间段内完成的服务器端Java操作数除以该时间段内的平均有功功率，此外，我们提出了一种虚拟机分配和迁移框架，该框架利用PPR来处理各种主机类型。通过在主机利用率和能耗之间实现最佳平衡，我们的框架能够确保主机计算机以最节能的利用率级别（即具有最高PPR的级别）运行，从而在显着降低性能的同时极大地降低了能耗。真实世界轨迹的实验表明，与三种基准节能VM分配和选择算法IqrMc，MadMm相比t和ThrRs，我们的框架能够将各种主机类型的能源消耗降低多达69.31％，同时迁移时间，停机时间更少，云计算数据中心的性能下降也很少。 （C）2019 Elsevier B.V.保留所有权利。

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  • 来源
    《Future generation computer systems》 |2019年第11期|380-394|共15页
  • 作者

    Ruan Xiaojun; Chen Haiquan; Tian Yun; Yin Shu;

  • 作者单位

    Calif State Univ East Bay Dept Comp Sci Hayward CA 94542 USA;

    Calif State Univ Sacramento Dept Comp Sci Sacramento CA 95819 USA;

    Calif State Univ Fullerton Dept Comp Sci Fullerton CA 92634 USA;

    ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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