Abstract
Data centers are growing rapidly in recent years. Data centers consume a huge amount of power, therefore how to save power is a key issue. Accurately predicting the power of virtual machine (VM) is significant to schedule VMs in different physical machines (PMs) to save power. Current researches rarely consider the impact of workload on this prediction. This paper studies the power prediction of VM under the multi-VM environment, with consideration of the impact of PMs’ workload. A RBF neural network approach is proposed to predict the VM’s power. Experiments show that the proposed approach is effective for VM’s power prediction and can achieve average error less than 2 %, which is smaller than those of comparative models.
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Acknowledgments
This work was supported in part by National Natural Science Foundation of China (61374204; 61375066).
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Xu, H., Zuo, X., Liu, C., Zhao, X. (2016). Predicting Virtual Machine’s Power via a RBF Neural Network. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_40
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DOI: https://doi.org/10.1007/978-3-319-41009-8_40
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