Cloud systems play a vital and significant role in our daily lives due to various internet services. For instance, email services, social networks, and others. Consequently, their energy consumption has also become an increasingly essential concern in Cloud Computing Systems (CCSs). Virtualization technologies are widely used to facilitate the management of CCSs and reduce their energy consumption. Virtualization enables live migration of Virtual Machines (VMs) where several VMs can be loaded on some Physical machines (PMs) called VM consolidation. A VM consolidation algorithm can be an effective technique for reducing energy consumption, operational cost, hardware cost, Service Level Agreements (SLAs) compliance/violation, CO2 emissions, and enhancing the hardware and service reliability, performance, and hardware lifetime, load balancing, and utilization in CCSs. Essentially, VM consolidation must minimize energy utilization and service quality in the cloud system. This study presents a taxonomy comprising resource assignment method, metrics, objective functions, migration methods, algorithmic methods, co-location criteria of VMs, architectures, workload dataset, and evaluation approaches in VM consolidation CCSs. Also, we reviewed related work regarding the resources of PMs, algorithm methods, metrics, architectures, and the objectives in static/dynamic VM consolidation.

由于各种互联网服务，云系统在我们的日常生活中起着至关重要的作用。例如，电子邮件服务，社交网络等。因此，它们的能源消耗也已成为云计算系统（CCS）中越来越重要的问题。虚拟化技术被广泛用于促进CCS的管理并降低其能耗。虚拟化可实现虚拟机（VM）的实时迁移，其中可以在某些物理机（PM）上加载多个VM，称为VM整合。VM整合算法是减少能耗，运营成本，硬件成本，服务水平协议（SLA）遵从/违反，CO2排放，增强硬件和服务可靠性，性能以及硬件寿命，负载平衡，和在CCS中的利用。本质上，VM整合必须最大程度地降低云系统中的能源利用率和服务质量。这项研究提出了一种分类法，包括资源分配方法，指标，目标函数，迁移方法，算法方法，VM的共置标准，体系结构，工作量数据集以及VM整合CCS中的评估方法。此外，我们回顾了有关PM资源，算法方法，指标，体系结构以及静态/动态VM整合目标的相关工作。VM整合CCS中的工作负载数据集和评估方法。此外，我们回顾了有关PM资源，算法方法，指标，体系结构以及静态/动态VM整合目标的相关工作。VM整合CCS中的工作负载数据集和评估方法。此外，我们回顾了有关PM资源，算法方法，指标，体系结构以及静态/动态VM整合目标的相关工作。