The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Energy Efficient Allocation of Virtual Machines in Cloud Data Centers
Abstract
Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with power-performance trade-off. We propose an efficient resource management policy for virtualized Cloud data centers. The objective is to continuously consolidate VMs leveraging live migration and switch off idle nodes to minimize power consumption, while providing required Quality of Service. We present evaluation results showing that dynamic reallocation of VMs brings substantial energy savings, thus justifying further development of the proposed policy.
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Citations
Cites
Buyya, R., Yeo, C. S., & Venugopal, S. (2008) · Cited by 1,792
Self-citationBuyya, R., Yeo, C. S., & Venugopal, S. (2008) · 2008 10th IEEE International Conference on High Performance Computing and Communications · doi:10.1109/hpcc.2008.172 · Cited by 1,156
Self-citationBuyya, R., Ranjan, R., & Calheiros, R. N. (2009) · 2009 International Conference on High Performance Computing & Simulation · doi:10.1109/hpcsim.2009.5192685 · Cited by 1,099
Self-citationEnergy aware consolidation for cloud computing
Srikantaiah, S., Kansal, A., & Zhao, F. (2008) · Cited by 784
Power and performance management of virtualized computing environments via lookahead control
Kusic, D., Kephart, J. O., Hanson, J. E., Kandasamy, N., & Jiang, G. (2009) · Cluster Computing · doi:10.1007/s10586-008-0070-y · Cited by 699
Report to Congress on Server and Data Center Energy Efficiency: Public Law 109-431
Alliance to Save Energy, & Ernest Orlando Lawrence Berkeley National Laboratory, B. (2007) · doi:10.2172/929723 · Cited by 589
Cited by
On Global Electricity Usage of Communication Technology: Trends to 2030
Andrae, A., & Edler, T. (2015) · Challenges · doi:10.3390/challe6010117 · Cited by 1,334
Optimizing energy consumption for data centers
Rong, H., Zhang, H., Xiao, S., Li, C., & Hu, C. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.283 · Cited by 398
Cloud Computing: Survey on Energy Efficiency
Mastelic, T., Oleksiak, A., Claussen, H., Brandic, I., Pierson, J.-M., & Vasilakos, A. V. (2015) · ACM Computing Surveys · doi:10.1145/2656204 · Cited by 276
Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach
Kansal, N. J., & Chana, I. (2016) · Journal of Grid Computing · doi:10.1007/s10723-016-9364-0 · Cited by 151
Metadata
- Authors
- Beloglazov, A., & Buyya, R.
- Venue
- 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
- Publisher
- IEEE
- Published
- 2010
- Pages
- 577-578
- OpenAlex
- W2157147632
- Cited by
- 422
- References
- 6
- FWCI
- 15.97
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
