The rapid rise in the cloud service adoption reflects the growth of Cloud Data Centers' (CDCs) number, size, energy consumption and eco-unfriendly carbon footprints. In CDCs, Virtual Machine Consolidation (VMC) plays a significant role in reducing their energy consumption and thereby reducing the carbon footprints. The state-of-the-art VMC heuristics based on First-Fit Decreasing (FFD) and Dominant Residual Resource (DRR) called DRR-FFD and DRR-BinFill are grouping the VMs based on single VM resource. We attempt to further reduce the energy consumption of CDCs through the proposed EMC2, an energy-efficient VMC framework that employs our multi-resource-fairness based VM selection heuristics, namely VMNeAR-H (Hierarchical), VM NeAR- D (Directed Hierarchical) and VM NeAR-E (Euclidean Distance). A dataset extracted from ENERGY STAR® containing the heterogeneous physical machine resource capacities and their estimated energy consumptions is utilised in the simulation experiments. The proposed EMC2-VMNeAR-D heuristic dominates the existing DRR heuristics in terms of total energy consumed by all the physical machines in the CDC (3.318 % energy savings on average of 40 schedules = 185107 kWh).

云服务采用率的迅速上升反映了云数据中心（CDC）的数量，规模，能耗和对生态不利的碳足迹的增长。在CDC中，虚拟机整合（VMC）在降低其能耗并从而减少碳足迹方面发挥着重要作用。基于先验递减（FFD）和显性剩余资源（DRR）（称为DRR-FFD和DRR-BinFill）的最新VMC启发式算法基于单个VM资源对VM进行分组。我们尝试通过提议的EMC2进一步降低CDC的能耗，EMC2是一种节能VMC框架，采用了我们基于多资源公平的VM选择启发式算法，即VMNeAR-H（分层），VM NeAR-D（定向分层）和VM NeAR-E（欧几里得距离）。从ENERGYSTAR®提取的数据集包含异构物理机器资源容量及其估计的能耗，用于模拟实验。就CDC中所有物理机消耗的总能量而言，拟议的EMC2-VMNeAR-D启发式方法在现有DRR启发式方法中占主导地位（平均40个计划的节能量为3.318％= 185107 kWh）。