Virtual machine power measuring technique with bounded error in cloud environments

  • Authors:

  • Peng Xiao;Zhigang Hu;Dongbo Liu;Guofeng Yan;Xilong Qu

  • Affiliations:

  • School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;School of Information Science and Engineering, Central South University, Changsha 410083, China;School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China;School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, China

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In virtualized datacenters, accurately measuring the power consumption of virtual machines (VMs) is the prerequisite to achieve the goal of fine-grained power management. However, existing VM power models can only provide power measurements with empirical accuracy and unbounded error. In this paper, we firstly formulize the co-relation between utilization and accuracy of power model, and compare two classes of VM power models; then we propose a novel VM power model which is based on a conception named relative performance monitoring counter (PMC); finally, based on the relative PMC power model, we propose a novel VM scheduling algorithm which uses the information of relative PMC to compensate the recursive power consumption. Theoretical analysis indicates that the proposed algorithm can provide bounded error when measuring per-VM power consumption. Extensive experiments are conducted by using various benchmarks on different platforms, and the results show that the error of per-VM power measurement can be significantly reduced. In addition, the proposed algorithm is effective to improve the power efficiency of a server when its virtualization ratio is high.