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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
U.S. Environmental Protection Agency ENERGY STAR Program: Server and Data Center Energy Efficiency Public Law 109-431 (2007)
NIST: The NIST definition of cloud computing. National Institute of Standards and Technology Special Publication 800-145, pp. 1–7 (2011)
Zuo, X., Zhang, G., Tan, W.: Self-adaptive learning PSO based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Trans. Autom. Sci. Eng. 11(2), 564–573 (2014)
Dhiman, G., Mihic, K., Rosing, T.: A system for online power prediction in virtualized environments using Gaussian mixture models. In: ACM/IEEE Design Automation Conference, Anaheim, pp. 807–812 (2010)
Li, Y.F., Wang, Y., Yin, B., Guan, L.: An online power metering model for cloud environment. In: IEEE International Symposium on Network Computing and Application, Cambridge, pp. 175–180 (2012)
Kansal, A., Zhao, F., Liu, J., Kothari, N.: Virtual machine power metering and provisioning. In: ACM Symposium on Cloud Computing, New York, pp. 39–50 (2010)
Wen, C.J., Long, X., Yang, Y., Ni, F., Mu, Y.F.: System power model and virtual machine power metering for cloud computing pricing. In: International Conference on Intelligent System Design and Engineering Applications, Hong Kong, pp. 1379–1382 (2013)
Chen, Q., Grosso, P., Veldt, K.V.D., Laat, C.D., Hofman, R., Bal, H.: Profiling energy consumption of VMs for green cloud computing. In: IEEE International Conference on Dependable, Autonomic and Secure Computing, Sydney, pp. 768–775 (2011)
Smith, J.W., Khajeh-Hosseini, A., Ward, J.S., Sommervile, I.: Cloud monitor: profiling power usage. In: IEEE Conference on Cloud Computing, Hawaii, pp. 947–948 (2012)
Krishnan, B., Amur, H., Gavrilovska, A., Schwan, K.: VM power metering: feasibility and challenges. ACM SIGMETRICS Perform. Eval. Rev. 38(3), 56–60 (2010)
Bohra, A.E.H., Chaudhary, V.: VMeter: power modeling for virtualized clouds. In: IEEE International Symposium on Parallel and Distributed Processing, pp. 1–8. IEEE Press, Atlanta (2010)
Quesnel, F., Mehta, H.K., Menaud, J.M.: Estimating the power consumption of an idle virtual machine. In: Green Computing and Communications, Beijing, pp. 268–275 (2013)
Jiang, Z.X., Lu, C.Y., Cai, Y.S., Jiang, Z.Y., Ma, C.Y.: VPower: metering power consumption of VM. In: IEEE International Conference on Software Engineering and Service Science, Beijing, pp. 483–486 (2013)
Yang, H.L., Zhao, Q., Luan, Z.Z., Qian, D.P.: iMeter: an integrated VM power model based on performance profiling. Future Gener. Comput. Syst. 36, 267–286 (2014)
Linux Kernel. https://www.kernel.org
Sysstat. https://github.com/sysstat/sysstat
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (61374204; 61375066).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-41009-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41008-1
Online ISBN: 978-3-319-41009-8
eBook Packages: Computer ScienceComputer Science (R0)