Finding the best approach for virtual machine placement (VMP) in cloud infrastructure is one of the most important optimization problems. The obtained solution of this problem significantly impacts on costs, energy, performance, etc. Physical machine (PM) processing capacity and virtual machine (VM) workloads have played important roles in VMP. Besides, in recent years with the increasingly development of semiconductors industry, fabricated chips including multiple homogeneous or heterogeneous processing elements (PEs) are of interest. The latest produced chip contains several general-purpose cores side by side with reconfigurable fabrics (RF) which have been used for accelerated computing and performing on par with ASIC hardware. In this paper a methodology is proposed to design VMP algorithms using arbitrary PEs. Moreover, a novel algorithm to address VMP problem using RF elements in cloud infrastructure is proposed. The methodology includes discovering, evaluation environment, models, parameters extraction, limitations, adaptation, problem formulation and heuristic. Among those, parameters extraction has a critical role in the overall performance. The extracted parameters are employed to make decision about which PM is more appropriate for hosting the desired VM. According to simulation results on synthetic workloads our proposed VMP algorithm outperforms others in operation with our proposed cloud architecture model.

在云基础架构中寻找虚拟机放置 (VMP) 的最佳方法是最重要的优化问题之一。该问题的解决方案对成本、能源、性能等产生显着影响。物理机 (PM) 处理能力和虚拟机 (VM) 工作负载在 VMP 中发挥了重要作用。此外，近年来随着半导体工业的日益发展，包括多个同质或异质处理元件（PE）的制造芯片受到关注。最新生产的芯片包含多个通用内核和可重构结构 (RF)，可用于加速计算并与 ASIC 硬件相媲美。在本文中，提出了一种使用任意 PE 设计 VMP 算法的方法。而且，提出了一种在云基础设施中使用射频元件解决 VMP 问题的新算法。该方法包括发现、评估环境、模型、参数提取、限制、适应、问题制定和启发式。其中，参数提取对整体性能起着至关重要的作用。提取的参数用于决定哪个 PM 更适合托管所需的 VM。根据合成工作负载的仿真结果，我们提出的 VMP 算法在使用我们提出的云架构模型的操作中优于其他算法。参数提取对整体性能起着至关重要的作用。提取的参数用于决定哪个 PM 更适合托管所需的 VM。根据合成工作负载的仿真结果，我们提出的 VMP 算法在使用我们提出的云架构模型的操作中优于其他算法。参数提取对整体性能起着至关重要的作用。提取的参数用于决定哪个 PM 更适合托管所需的 VM。根据合成工作负载的仿真结果，我们提出的 VMP 算法在使用我们提出的云架构模型的操作中优于其他算法。