Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.

云提供商使用基础架构即服务（IaaS）服务模型根据用户需求提供计算服务。在云数据中心中，有可能使用虚拟化技术在物理机（PM）上运行多个虚拟机（VM）。虚拟机放置（VMP）问题是虚拟机跨多个物理虚拟机的映射。此过程在定义云数据中心基础架构中的能耗和资源使用效率方面起着至关重要的作用。但是，由于诸如机器异构，多维资源和大规模云数据中心之类的困难，提供有效的解决方案并非易事。在本文中，我们提出了一种有效的启发式算法，其重点在于功耗和资源浪费的优化，以解决上述问题。MinPR，通过减少活动的物理机的数量并优先考虑高能效的物理机，将总功耗降至最低。而且，它通过最大化和平衡物理机之间的资源利用率来减少资源浪费。为了实现这些目标，我们提出了一个新的资源使用系数模型，该模型使用奖励和惩罚机制来管理物理机上虚拟机的放置。基于云用户自定义VM和Amazon EC2实例工作负载的仿真表明，该算法优于现有方法。特别是，对于云用户定制的VM，所提出的算法可将总能耗降低多达15％，对于Amazon EC2实例，可降低多达10％。