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Towards Virtual Machine Energy-Aware Cost Prediction in Clouds

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Economics of Grids, Clouds, Systems, and Services (GECON 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10537))

Abstract

Pricing mechanisms employed by different service providers significantly influence the role of cloud computing within the IT industry. With the increasing cost of electricity, Cloud providers consider power consumption as one of the major cost factors to be maintained within their infrastructures. Consequently, modelling a new pricing mechanism that allow Cloud providers to determine the potential cost of resource usage and power consumption has attracted the attention of many researchers. Furthermore, predicting the future cost of Cloud services can help the service providers to offer the suitable services to the customers that meet their requirements. This paper introduces an Energy-Aware Cost Prediction Framework to estimate the total cost of Virtual Machines (VMs) by considering the resource usage and power consumption. The VMs’ workload is firstly predicted based on an Autoregressive Integrated Moving Average (ARIMA) model. The power consumption is then predicted using regression models. The comparison between the predicted and actual results obtained in a real Cloud testbed shows that this framework is capable of predicting the workload, power consumption and total cost for different VMs with good prediction accuracy, e.g. with 0.06 absolute percentage error for the predicted total cost of the VM.

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Author information

Authors and Affiliations

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

    Mohammad Aldossary & Karim Djemame

  2. Prince Sattam Bin Abdulaziz University, Al Kharj, Kingdom of Saudi Arabia

    Mohammad Aldossary

  3. Majmaah University, Al Majmaah, Kingdom of Saudi Arabia

    Ibrahim Alzamil

Authors
  1. Mohammad Aldossary
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  2. Ibrahim Alzamil
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  3. Karim Djemame
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Corresponding author

Correspondence to Mohammad Aldossary .

Editor information

Editors and Affiliations

  1. UPPA, LIUPPA laboratory, UFR Sciences et Techniques, Pau, France

    Congduc Pham

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

    Jörn Altmann

  3. Department of Computer Science, Universidad de Zaragoza, Zaragoza, Spain

    José Ángel Bañares

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Aldossary, M., Alzamil, I., Djemame, K. (2017). Towards Virtual Machine Energy-Aware Cost Prediction in Clouds. In: Pham, C., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2017. Lecture Notes in Computer Science(), vol 10537. Springer, Cham. https://doi.org/10.1007/978-3-319-68066-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-68066-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68065-1

  • Online ISBN: 978-3-319-68066-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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