dc.contributor.author |
Jangiti, Saikishor |
|
dc.date.accessioned |
2023-01-23T06:57:22Z |
|
dc.date.available |
2023-01-23T06:57:22Z |
|
dc.date.issued |
2020-09 |
|
dc.identifier.uri |
https://www.sciencedirect.com/science/article/pii/S2210537920301414 |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8651 |
|
dc.description.abstract |
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). |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Elsevier |
en_US |
dc.subject |
Computer Science |
en_US |
dc.subject |
Virtual machine consolidation |
en_US |
dc.subject |
Cloud computing |
en_US |
dc.subject |
First-fit decreasing |
en_US |
dc.subject |
Dominant resource fairness |
en_US |
dc.subject |
Residual resource ratio |
en_US |
dc.title |
EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres |
en_US |
dc.type |
Article |
en_US |