EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres

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 (R) 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). (C) 2020 Elsevier Inc. All rights reserved.