In a cloud computing environment, virtual machine placement (VMP) represents an important challenge to select the most suitable set of physical machines (PMs) to host a set of virtual machines (VMs). The challenge is how to find optimal or near-optimal solution effectively and efficiently especially when VMP is considered as a NP-hard problem. However, the existing algorithms have focused mostly on compute resources when provisioning VMs and ignore storage resources. Therefore, they often generate non-optimal compute and storage resources for executing users applications. To address this problem, we outline more in details the binary linear programming (BLP) model previously proposed to solve the consolidated VMP with disk anti-colocation constraint (denoted VMcP-DAC) and we solve it using a heuristic algorithm. Our approach considers a special type of disk anti-colocation requirements to prevent Input/Output (IO) performance bottleneck. We implement a variable neighborhood search based optimization heuristic (denoted VNS-H) to solve the VMcP-DAC by minimizing both the resource wastage and the operational expenditure. To the best of our knowledge, only three studies in the literature that are devoted to VMcP-DAC problem. In two of these three works, authors proposed exact algorithms that are unable to solve large scale VMcP-DAC problem instances. For this reason, in a previous work, we proposed a decomposition based method to overcome the convergence issues for only large scale problems. In the present paper, our goal is to solve VMcP-DAC problem instances suitable for both regular and large data centers. We investigate the effectiveness of the proposed VNS-H, showing that it has a better convergence characteristics and it is more computationally efficient than compared methods from the literature.

在云计算环境中，虚拟机放置（VMP）代表了一项重要挑战，即选择最合适的一组物理机（PM）来承载一组虚拟机（VM）。面临的挑战是如何有效，高效地找到最佳或接近最佳的解决方案，尤其是在将VMP视为NP难题的情况下。但是，现有的算法在配置VM时主要集中在计算资源上，而忽略存储资源。因此，它们通常会生成非最佳的计算和存储资源来执行用户应用程序。为解决此问题，我们将更详细地概述以前为解决具有磁盘反定位约束的合并VMP（表示为VMcP-DAC）而提出的二进制线性规划（BLP）模型，并使用启发式算法对其进行求解。我们的方法考虑了特殊类型的磁盘防托管要求，以防止输入/输出（IO）性能瓶颈。我们实现了一种基于可变邻域搜索的优化试探法（表示为VNS-H），以通过最小化资源浪费和运营支出来解决VMcP-DAC。据我们所知，文献中只有三篇专门研究VMcP-DAC问题。在这三项工作中的两项中，作者提出了无法解决大规模VMcP-DAC问题实例的精确算法。因此，在先前的工作中，我们提出了一种基于分解的方法来克服仅针对大规模问题的收敛性问题。在本文中，我们的目标是解决适用于常规数据中心和大型数据中心的VMcP-DAC问题实例。