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

摘要

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.