One of the main challenges in cloud computing is an enormous amount of energy consumed in data-centers. Several researches have been conducted on Virtual Machine(VM) consolidation to optimize energy consumption. Among the proposed VM consolidations, OpenStack Neat is notable for its practicality. OpenStack Neat is an open-source consolidation framework that can seamlessly integrate to OpenStack, one of the most common and widely used open-source cloud management tool. The framework has components for deciding when to migrate VMs and for selecting suitable hosts for the VMs (VM placement). The VM placement algorithm of OpenStack Neat is called Modified Best-Fit Decreasing (MBFD). MBFD is based on a heuristic that handles only minimizing the number of servers. The heuristic is not only less energy efficient but also increases Service Level Agreement (SLA) violation and consequently cause more VM migrations. To improve the energy efficiency, we propose VM placement algorithms based on both bin-packing heuristics and servers’ power efficiency. In addition, we introduce a new bin-packing heuristic called a Medium-Fit (MF) to reduce SLA violation. To evaluate performance of the proposed algorithms we have conducted experiments using CloudSim on three cloud data-center scenarios: homogeneous, heterogeneous and default. Workloads that run in the data-centers are generated from traces of PlanetLab and Bitbrains clouds. The results of the experiment show up-to 67% improvement in energy consumption and up-to 78% and 46% reduction in SLA violation and amount of VM migrations, respectively. Moreover, all improvements are statistically significant with significance level of 0.01.

中文翻译：

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用于OpenStack Neat整合框架的节能型VM放置算法

云计算的主要挑战之一是数据中心消耗的大量能源。为了优化能耗，已经对虚拟机（VM）整合进行了多项研究。在提议的虚拟机整合中，OpenStack Neat的实用性着称。OpenStack Neat是一个开源整合框架，可以无缝集成到OpenStack，OpenStack是最常见且使用最广泛的开源云管理工具之一。该框架具有决定何时迁移VM以及为VM选择合适的主机（VM放置）的组件。OpenStack Neat的VM放置算法称为“修改最佳拟合减少（MBFD）”。MBFD基于一种试探法，该试探法仅处理最少的服务器数量。启发式方法不仅降低了能源效率，而且增加了违反服务水平协议（SLA）的后果，因此导致了更多的VM迁移。为了提高能效，我们提出了基于装箱启发法和服务器能效的VM放置算法。此外，我们引入了一种新的垃圾箱启发式方法，称为中型（MF），以减少违反SLA的情况。为了评估所提出算法的性能，我们使用CloudSim在三种云数据中心场景中进行了实验：同构，异构和默认。数据中心中运行的工作负载是从PlanetLab和Bitbrains云的痕迹生成的。实验结果表明，能耗降低了多达67％，SLA违规和VM迁移量分别降低了78％和46％。此外，