Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

最小化资源浪费可以降低数据中心运营的能源成本，但也可能导致相当高的资源超额使用量，从而影响正在运行的应用程序的服务质量（QoS）。资源浪费和超额使用之间的有效折衷是虚拟化云中的一项艰巨任务，它取决于将虚拟机（VM）分配给物理资源。我们在本文中提出了一种用于虚拟机动态放置的多目标方法，该方法利用实时迁移机制来同时优化资源浪费，超额使用率和迁移能量。我们的优化算法使用了一种新颖的基于岛种群模型的进化元启发式算法，以较高的精度和多样性来逼近帕累托最优的VM布局集合。