Virtual machine (VM) placement is a key technique for energy optimization in cloud data centers. Previous work generally focus on how to place the VMs efficiently in servers to optimize the physical resources used (e.g., memory, bandwidth, CPU, etc.), network resources used or cooling energy consumption. These work can optimize the energy consumption of cloud data centers according to one or two aspects (e.g. server, network or cooling), however, these methods may cause increased energy consumption in other aspects. To address this problem, we propose a global-energy-aware VMP (virtual machine placement) strategy to reduce, from multiple aspects, the total energy consumption of data centers. A two-step SAG algorithm is designed to lower the energy consumption of cloud data centers where multiple VMs are deployed. We conduct extensive experiments to evaluate the effectiveness of SAG. Two workloads from real-world data centers are utilized to quantitatively measure and compare the performance of our SAG with other typical algorithms. Experimental results indicate that, compared to other algorithms, our global-energy-aware VMP strategy can reduce the total energy consumption of the cloud data center by 8%–24.9%.

虚拟机（VM）放置是云数据中心能源优化的一项关键技术。先前的工作通常集中在如何有效地将VM放置在服务器中以优化所使用的物理资源（例如，内存，带宽，CPU等），所使用的网络资源或降低能耗。这些工作可以根据一个或两个方面（例如，服务器，网络或冷却）优化云数据中心的能耗，但是，这些方法可能会导致其他方面的能耗增加。为了解决这个问题，我们提出了一种全球节能的VMP（虚拟机放置）策略，从多个方面减少数据中心的总能耗。设计了两步式SAG算法，以降低部署了多个VM的云数据中心的能耗。我们进行了广泛的实验，以评估SAG的有效性。来自现实世界数据中心的两个工作负载被用来定量测量和比较我们的SAG与其他典型算法的性能。实验结果表明，与其他算法相比，我们的全球能源感知VMP策略可以将云数据中心的总能耗降低8％–24.9％。