With energy consumption in high-performance computing clouds growing rapidly, energy saving has become an important topic. Virtualization provides opportunities to save energy by enabling one physical machine (PM) to host multiple virtual machines (VMs). Dynamic voltage and frequency scaling (DVFS) is another technology to reduce energy consumption. However, in heterogeneous cloud environments where DVFS may be applied at the chip level or the core level, it is a great challenge to combine these two technologies efficiently. On per-core DVFS servers, cloud managers should carefully determine VM placements to minimize performance interference. On full-chip DVFS servers, cloud managers further face the choice of whether to combine VMs with different characteristics to reduce performance interference or to combine VMs with similar characteristics to take better advantage of DVFS. This paper presents a novel mechanism combining a VM placement algorithm and a frequency scaling method. We formulate this VM placement problem as an integer programming (IP) to find appropriate placement configurations, and we utilize support vector machines to select suitable frequencies. We conduct detailed experiments and simulations, showing that our scheme effectively reduces energy consumption with modest impact on performance. Particularly, the total energy delay product is reduced by up to 60%.

中文翻译：

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平衡HPC云中的功能和性能

随着高性能计算云中能耗的快速增长，节能已成为重要的话题。通过使一台物理机（PM）托管多个虚拟机（VM），虚拟化提供了节省能源的机会。动态电压和频率缩放（DVFS）是另一种降低能耗的技术。但是，在可以在芯片级或核心级应用DVFS的异构云环境中，有效地结合这两种技术是一个巨大的挑战。在每核DVFS服务器上，云管理人员应仔细确定VM的位置，以最大程度地降低性能干扰。在全芯片DVFS服务器上，云管理者还面临着选择是将具有不同特征的VM组合以减少性能干扰，还是将具有相似特征的VM组合以更好地利用DVFS的选择。本文提出了一种新颖的机制，结合了虚拟机布局算法和频率缩放方法。我们将此VM放置问题公式化为整数编程（IP），以找到合适的放置配置，并利用支持向量机选择合适的频率。我们进行了详细的实验和模拟，表明我们的方案有效地降低了能耗，并且对性能的影响适中。特别是，总的能量延迟积减少了多达60％。我们将此VM放置问题公式化为整数编程（IP），以找到合适的放置配置，并利用支持向量机选择合适的频率。我们进行了详细的实验和模拟，表明我们的方案有效地降低了能耗，并且对性能的影响适中。特别是，总的能量延迟积减少了多达60％。我们将此VM放置问题公式化为整数编程（IP），以找到合适的放置配置，并利用支持向量机选择合适的频率。我们进行了详细的实验和模拟，表明我们的方案有效地降低了能耗，并且对性能的影响适中。特别是，总的能量延迟积减少了多达60％。