An Effective Energy Testing Framework for Cloud Workflow Activities

  • Zhou Zhao
  • Xiao LiuEmail author
  • Juan Li
  • Kepi Zhang
  • Jin Liu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 602)


Cloud computing as the latest computing paradigm has shown its promising future in business workflow systems facing massive concurrent user requests and complicated computing tasks. With the fast growth of cloud data centers, energy management especially energy monitoring and saving in cloud workflow systems has been attracting increasing attention. It is obvious that the energy for running a cloud workflow instance is mainly dependent on the energy for executing its workflow activities. However, existing energy management strategies mainly monitor the virtual machines instead of the workflow activities running on them, and hence it is difficult to directly monitor and optimize the energy consumption of cloud workflows. To address such an issue, in this paper, we propose an effective energy testing framework for cloud workflow activities. This framework can help to accurately test and analyze the baseline energy of physical and virtual machines in the cloud environment, and then obtain the energy consumption data of cloud workflow activities. Based on these data, we can further produce the energy consumption model and apply energy prediction strategies. Our experiments are conducted in an OpenStack based cloud computing environment. The effectiveness of our framework has been successfully verified through a detailed case study and a set of energy modelling and prediction experiments based on representative time-series models.


Cloud computing Energy testing Business workflow Time-series model 



The research work reported in this paper is partly supported by National Natural Science Foundation of China (NSFC) under No. 61300042, and Shanghai Knowledge Service Platform Project No. ZF1213.


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Zhou Zhao
    • 1
  • Xiao Liu
    • 1
    Email author
  • Juan Li
    • 2
  • Kepi Zhang
    • 1
  • Jin Liu
    • 2
  1. 1.Shanghai Key Lab for Trustworthy ComputingEast China Normal UniversityShanghaiChina
  2. 2.State Key Lab for Software EngineeringWuhan UniversityWuhanChina

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