A global-energy-aware virtual machine placement strategy for cloud data centers

Abstract 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%.