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Energy Prediction for Cloud Workload Patterns

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Economics of Grids, Clouds, Systems, and Services (GECON 2016)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10382))

Abstract

The excessive use of energy consumption in Cloud infrastructures has become one of the major cost factors for Cloud providers to maintain. In order to enhance the energy efficiency of Cloud resources, proactive and reactive management tools are used. However, these tools need to be supported with energy-awareness not only at the physical machine (PM) level but also at virtual machine (VM) level in order to enhance decision-making. This paper introduces an energy-aware profiling model to identify energy consumption for heterogeneous and homogeneous VMs running on the same PM and presents an energy-aware prediction framework to forecast future VMs energy consumption. This framework first predicts the VMs’ workload based on historical workload patterns using Autoregressive Integrated Moving Average (ARIMA) model. The predicted VM workload is then correlated to the physical resources within this framework in order to get the predicted VM energy consumption. Compared with actual results obtained in a real Cloud testbed, the predicted results show that this energy-aware prediction framework can get up to 2.58 Mean Percentage Error (MPE) for the VM workload prediction, and up to −4.47 MPE for the VM energy prediction based on periodic workload pattern.

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Author information

Authors and Affiliations

  1. School of Computing, University of Leeds, Leeds, UK

    Ibrahim Alzamil & Karim Djemame

  2. College of Science and Humanities, Majmaah University, Alghat, Kingdom of Saudi Arabia

    Ibrahim Alzamil

Authors
  1. Ibrahim Alzamil
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  2. Karim Djemame
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Corresponding author

Correspondence to Ibrahim Alzamil .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Zaragoza, Zaragoza, Spain

    José Ángel Bañares

  2. Harokopio University, Tavros, Greece

    Konstantinos Tserpes

  3. Seoul National University, Seoul, Korea (Republic of)

    Jörn Altmann

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Alzamil, I., Djemame, K. (2017). Energy Prediction for Cloud Workload Patterns. In: Bañares, J., Tserpes, K., Altmann, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2016. Lecture Notes in Computer Science(), vol 10382. Springer, Cham. https://doi.org/10.1007/978-3-319-61920-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-61920-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61919-4

  • Online ISBN: 978-3-319-61920-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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