De Maio, V, Kecskemeti, G and Prodan, R (2015) A workload-aware energy model for virtual machine migration. In: Proceedings - IEEE International Conference on Cluster Computing, ICCC , 2015-O. pp. 274-283. (2015 IEEE International Conference on Cluster Computing, 8-11 Sept 2015, Chicago IL).

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Abstract

Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction. © 2015 IEEE.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	IEEE
Date Deposited:	09 Aug 2016 09:04
Last Modified:	13 Apr 2022 15:14
DOI or ID number:	10.1109/CLUSTER.2015.47
URI:	https://researchonline.ljmu.ac.uk/id/eprint/3987

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