In cloud data centers, an appropriate Virtual Machine (VM) placement has become an effective method to improve the resource utilization and reduce the energy consumption. However, most current solutions regard the VM placement as a bin-packing problem and each VM is seen as a single object. None of them have taken the relationships between VMs into consideration, which supplies us with a kind of new perspective. In this paper, we provide a model which explores the relationships for every two VMs based on the resource requirement provided by ARIMA prediction. This model evaluates the volatility of resource utilization after putting the two VMs on the same host and we call this model as affinity model. Based on the affinity model, VMs will be placed on those hosts that have the highest affinity with them. Therefore, we call it as Predicted Affinity based Virtual Machine Placement Algorithm (PAVMP). The advantages of PAVMP are showed by comparing it with other VM placement algorithms on CloudSim simulation platform with the PlanetLab and Google workload trace.

中文翻译：

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云计算环境中基于预测关联的虚拟机放置

在云数据中心，适当的虚拟机（VM）放置已成为提高资源利用率和降低能耗的有效方法。但是，大多数当前的解决方案将 VM 放置视为装箱问题，并且每个 VM 都被视为单个对象。它们都没有考虑到虚拟机之间的关系，这为我们提供了一种新的视角。在本文中，我们提供了一个模型，该模型基于 ARIMA 预测提供的资源需求来探索每两个 VM 的关系。该模型评估了将两个 VM 放在同一主机上后资源利用率的波动性，我们将此模型称为亲和性模型。根据关联模型，VM 将放置在与它们具有最高关联的主机上。所以，我们将其称为基于预测亲和力的虚拟机放置算法 (PAVMP)。通过在 CloudSim 仿真平台上使用 PlanetLab 和 Google 工作负载跟踪将 PAVMP 与其他 VM 放置算法进行比较，显示了 PAVMP 的优势。