An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment
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.
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