An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment
- 309 Downloads
Cloud computing has gained enormous popularity by providing high availability and scalability as well as on-demand services. However, with the continuous rise of energy consumption cost, the virtualized environment of cloud data centers poses a challenge to today’s power monitoring system. Software-based power monitoring is gaining prevalence since power models can work precisely by exploiting soft computing methodologies like genetic programming and swarm intelligence for model optimization. However, traditional power models barely consider virtualization and have drawbacks like high error rate, low feasibility as well as insufficient scalability. In this paper, we first analyze the power signatures of virtual machines in different configurations through experiments. Then we propose a virtual machine (VM) power model, named CAM, which is able to adapt to the reconfiguration of VMs and provide accurate power estimating under CPU-intensive workload. We also propose two training methodologies corresponding to two typical situations for model training. CAM can estimate the power of a single VM as well as a physical server hosting several heterogeneous VMs. We exploited public Linux benchmarks to evaluate CAM .The experimental results show that CAM produced very small errors in power estimating for both VMs (4.26 % on average) and the host server (0.88 % on average).
KeywordsCloud computing Power consumption Virtual machine vCPU Power model
Thanks to the helpful comments and suggestions from the anonymous reviewers. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61402183 and 61272382), Guangdong Natural Science Foundation (Grant No. S2012030006242), Guangdong Pro- vincial Scientific and Technological Projects (Grant Nos. 2014B 010117001, 2014A010103022, 2014A010103008, 2013B010202001 and 2013B090200021) and Fundamental Research Funds for the Central Universities, SCUT(No. 2015ZZ0098).
Compliance with ethical standards
This study was funded by the National Natural Science Foundation of China (Grant Nos. 61402183 and 61272382), Guangdong Natural Science Foundation (Grant No. S2012030006242), Guangdong Pro vincial Scientific and Technological Projects (Grant Nos. 2014B 010117001, 2014A010103022, 2014A010103008, 2013B010202001 and 2013B090200021) and Fundamental Research Funds for the Central Universities, SCUT (No. 2015ZZ0098).
Conflict of interest
Wentai Wu declares that he has no conflict of interest. Weiwei Lin declares that he has no conflict of interest. Zhiping Peng declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Basmadjian R, Ali N, Niedermeier F, Meer HD, Giuliani G (2011) A methodology to predict the power consumption of servers in data centres. In: Proceedings of the 2nd international conference on energy-efficient computing and networking. ACM press, New York, pp 1–10. doi:10.1145/2318716.2318718
- Chen FF, Schneider JG, Yang Y, Grundy J, He Q (2012) An energy consumption model and analysis tool for cloud computing environment. In: Proceedings of the first international workshop on green and sustainable software. IEEE press, New York, pp 45–50. doi:10.1109/GREENS.2012.6224255
- Colmant M, Kurpicz M, Felber P, Huertas L, Rouvoy R, Sobe A (2015) Process-level power estimation in VM-based systems. In: Proceedings of the tenth European conference on computer systems (EuroSys’15), Apr 2015, Bordeaux, France. ACM 14, pp 1–14. doi:10.1145/2741948.2741971
- Hamilton J (2009) Cooperative expendable micro-slice servers (CEMS): low cost, low power servers for internet-scale services. In: Proceedings of the conference on innovative data systems research (CIDR’09). http://dblp.uni-trier.de/db/conf/cidr/cidr2009.html
- Hsu CH, Poole SW (2011) Power signature analysis of the SPECpower\_ssj2008 benchmark. In: Proceedings of 2011 IEEE international symposium on performance analysis of systems and software (ISPASS). IEEE COMPUTER SOC press, Los Alamitos, pp 227–236. doi:10.1109/ISPASS.2011.5762739
- Kansal A, Zhao F, Liu J, Kothari N, Bhattacharya A A (2010) Virtual machine power metering and provisioning. In: Proceedings of the 1st ACM symposium on cloud computing. ACM press, New York, pp 39–50. doi:10.1145/1807128.1807136
- Kim N, Cho J, Seo E (2011) Energy-based accounting and scheduling of virtual machines in a cloud system. In: Proceedings of 2011 IEEE/ACM international conference on green computing and communications (GreenCom). IEEE press, New York, pp 176–181. doi:10.1109/GreenCom.37
- Lin WW, Tan L, Wang JZ (2014) Novel resource allocation algorithm for energy-efficient cloud computing in heterogeneous environment. Int J Grid High Perform Comput (IJGHPC) 6(1):63–76. doi:10.4018/ijghpc.2014010104
- Lin WW, Xu SY, Li J, Xu LL, Peng ZP (2015a) Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput. doi:10.1007/s00500-015-1862-7
- Lin WW, Zhu CY, Li J, Liu B, Lian H (2015b) Novel algorithms and equivalence optimisation for resource allocation in cloud computing. Int J Web Grid Serv 11(2):193–210. doi:10.1504/IJWGS.2015.068899