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A Decision Support Model for Cloud Bursting

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  • Markus Lilienthal

Abstract

The cloud computing market divides into public (commercial) and private (self-provisioned) clouds. The concept of cloud bursting combines public and private clouds: The private cloud (internal resources) provides the computational capacity, but a part of the demand is offloaded onto public clouds. This article proposes an easy-to-apply economic decision support model for determining on the one hand the optimal size of the internal capacity for cloud bursting technology, and on the other hand the cost savings. The model uses an expected value approach that considers stochastic workload and is flexible with respect to the distribution choice. Two empirical examples demonstrate the applicability of the model. Copyright Springer Fachmedien Wiesbaden 2013

Suggested Citation

  • Markus Lilienthal, 2013. "A Decision Support Model for Cloud Bursting," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(2), pages 71-81, April.
    • Handle: RePEc:spr:binfse:v:5:y:2013:i:2:p:71-81
    DOI: 10.1007/s12599-013-0257-5
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    References listed on IDEAS

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    1. Beulen, Erik & Fenema, Paul Van & Currie, Wendy, 2005. "From Application Outsourcing to Infrastructure Management:: Extending the Offshore Outsourcing Service Portfolio," European Management Journal, Elsevier, vol. 23(2), pages 133-144, April.
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    3. Paul A. Weekes & John C. Chambers & Satinder K. Mullick, 1969. "Lease-Buy Planning Decisions," Management Science, INFORMS, vol. 15(6), pages 295-307, February.
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    Cited by:

    1. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 2017. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 19(5), pages 1085-1099, October.
    2. Sebastian Büsch & Volker Nissen & Arndt Wünscher, 0. "Automatic classification of data-warehouse-data for information lifecycle management using machine learning techniques," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    3. Robert Keller & Lukas Häfner & Thomas Sachs & Gilbert Fridgen, 2020. "Scheduling Flexible Demand in Cloud Computing Spot Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(1), pages 25-39, February.

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