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Dynamic Resource Allocation Through Workload Prediction for Energy Efficient Computing

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Advances in Computational Intelligence Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 513))

Abstract

Rapid and continuous increase in online information exchange and data based services has led to an increase in enterprise data centres. Energy efficient computing is key to a cost effective operation for all such enterprise IT systems. In this paper we propose dynamic resource allocation in server based IT systems through workload prediction for energy efficient computing. We use CPU core as a dynamic resource that can be allocated and deallocated based on predicted workload. We use online workload prediction as opposed to offline statistical analysis of workload characteristics. We use online learning and workload prediction using neural network for online dynamic resource allocation for energy efficient computing. We also analyse the effect of dynamic resource allocation on clients by measuring the request response time to clients for variable number of cores in operation. We show that dynamic resource allocation through workload prediction in server based IT systems can provide a cost effective, energy efficient and reliable operation without effecting quality of experience for clients.

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Author information

Authors and Affiliations

  1. Institute of Industrial Research, University of Portsmouth, 36–40 Middle Street, Portsmouth, PO5 4BP, UK

    Adeel Ahmed & David J. Brown

  2. School of Computing, University of Portsmouth, Buckingham Building, Lion Terrace, Portsmouth, PO1 3HE, UK

    Alexander Gegov

Authors
  1. Adeel Ahmed
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  2. David J. Brown
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  3. Alexander Gegov
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Corresponding author

Correspondence to Adeel Ahmed .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University Bailrigg School of Computing and Communications, Lancaster, United Kingdom

    Plamen Angelov

  2. School of Computing, University of Portsmouth School of Computing, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  3. School of Comp. Sci. & Digital Media, Robert Gordon University School of Comp. Sci. & Digital Media, Aberdeen, United Kingdom

    Chrisina Jayne

  4. Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth University Ins. of Mathematics, Physics & Comp. Sci, Aberystwyth, United Kingdom

    Qiang Shen

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© 2017 Springer International Publishing AG

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Ahmed, A., Brown, D.J., Gegov, A. (2017). Dynamic Resource Allocation Through Workload Prediction for Energy Efficient Computing. In: Angelov, P., Gegov, A., Jayne, C., Shen, Q. (eds) Advances in Computational Intelligence Systems. Advances in Intelligent Systems and Computing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-319-46562-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-46562-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46561-6

  • Online ISBN: 978-3-319-46562-3

  • eBook Packages: EngineeringEngineering (R0)

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