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Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment Public Deposited

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Abstract
  • Energy consumption has become a significant concern for cloud service providers due to financial as well as environmental factors. As a result, cloud service providers are seeking innovative ways that allow them to reduce the amount of energy that their data centers consume. They are calling for the development of new energy-efficient techniques that are suitable for their data centers. The services offered by the cloud computing paradigm have unique characteristics that distinguish them from traditional services, giving rise to new design challenges as well as opportunities when it comes to developing energy-aware resource allocation techniques for cloud computing data centers. In this article we highlight key resource allocation challenges, and present some potential solutions to reduce cloud data center energy consumption. Special focus is given to power management techniques that exploit the virtualization technology to save energy. Several experiments, based on real traces from a Google cluster, are also presented to support some of the claims we make in this article.
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  • Dabbagh, M., Hamdaoui, B., Guizani, M., & Rayes, A. (2015). Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment. IEEE Network, 29(2), 56-61. doi:10.1109/MNET.2015.7064904
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  • 29
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  • 2
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  • This work was made possible by NPRP grant # NPRP 5-319-2-121 from the Qatar National Research Fund (a member of Qatar Foundation).
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  • description.provenance : Approved for entry into archive by Deanne Bruner(deanne.bruner@oregonstate.edu) on 2015-10-14T17:51:53Z (GMT) No. of bitstreams: 1 DabbaghMehiarElectricalEngineeringComputerScienceTowardEnergyEfficientCloudComputing.pdf: 2800133 bytes, checksum: 217519445b750b1775f4836abf58ef97 (MD5)
  • description.provenance : Submitted by Deanne Bruner (deanne.bruner@oregonstate.edu) on 2015-10-14T17:51:02Z No. of bitstreams: 1 DabbaghMehiarElectricalEngineeringComputerScienceTowardEnergyEfficientCloudComputing.pdf: 2800133 bytes, checksum: 217519445b750b1775f4836abf58ef97 (MD5)
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