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Energy Consumption Prediction Based on Time-Series Models for CPU-Intensive Activities in the Cloud

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Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9531))

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Abstract

Due to the increasing energy consumption in cloud data centers, energy saving has become a vital objective in designing the underlying cloud infrastructures. A precise energy consumption model is the foundation of many energy-saving strategies. This paper focuses on exploring the energy consumption of virtual machines running various CPU-intensive activities in the cloud server using two types of models: traditional time-series models, such as ARMA and ES, and time-series segmentation models, such as sliding windows model and bottom-up model. We have built a cloud environment using OpenStack, and conducted extensive experiments to analyze and compare the prediction accuracy of these strategies. The results indicate that the performance of ES model is better than the ARMA model in predicting the energy consumption of known activities. When predicting the energy consumption of unknown activities, sliding windows segmentation model and bottom-up segmentation model can all have satisfactory performance but the former is slightly better than the later.

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Acknowledgments

This work was partially supported by the grants of the National Natural Science Foundation of China (61572374, 61070013, U1135005) and the Fundamental Research Funds for the Central Universities (No. 2042014kf0272, No. 2014211020201).

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China

    Juan Li & Jin Liu

  2. Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, 200241, China

    Xiao Liu & Zhou Zhao

Authors
  1. Juan Li
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  2. Xiao Liu
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  3. Zhou Zhao
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  4. Jin Liu
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Corresponding author

Correspondence to Jin Liu .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University, Changsha, China

    Kenli Li

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Li, J., Liu, X., Zhao, Z., Liu, J. (2015). Energy Consumption Prediction Based on Time-Series Models for CPU-Intensive Activities in the Cloud. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9531. Springer, Cham. https://doi.org/10.1007/978-3-319-27140-8_52

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  • DOI: https://doi.org/10.1007/978-3-319-27140-8_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27139-2

  • Online ISBN: 978-3-319-27140-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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