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Comparison of Two Yield Management Strategies for Cloud Service Providers

Author

Listed:
  • Mohammad Mahdi Kashef

    () (TEMEP, College of Engineering, Seoul National University)

  • Azamat Uzbekov

    () (TEMEP, College of Engineering, Seoul National University)

  • Jorn Altmann

    () (TEMEP, College of Engineering, Seoul National University)

  • Matthias Hovestadt

    () (Department of Computer Science, Hanover University of Applied Sciences)

Abstract

Several Cloud computing business models have been developed and implemented, including dynamic pricing schemes. This paper extends the known concepts of revenue management to the specific case of Cloud computing from two perspectives. First, we propose system architecture for Cloud service providers for combining demand-based pricing and scheduling. Second, a comparison of two yield management methods for cloud computing has been compared: Limited Discount Period Algorithm and VM Reservation Level Algorithm. By taking advantage of demand estimation, the two algorithms find the optimum number of VMs that are sold at full price and the optimum time period before the allocation when the prices should change. Simulation results show that both yield management methods outperform static pricing models and the algorithms perform differently considering the deviation of demand.

Suggested Citation

  • Mohammad Mahdi Kashef & Azamat Uzbekov & Jorn Altmann & Matthias Hovestadt, 2013. "Comparison of Two Yield Management Strategies for Cloud Service Providers," TEMEP Discussion Papers 2013103, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised May 2013.
  • Handle: RePEc:snv:dp2009:2013103
    as

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    File URL: ftp://147.46.237.98/DP-103.pdf
    File Function: First version, 2013
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    References listed on IDEAS

    as
    1. Jorn Altmann & Matthias Hovestadt & Odej Kao, 2011. "Business Support Service Platform for Providers in Open Cloud Computing Markets," TEMEP Discussion Papers 201179, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2011.
    2. Han, Lei, 2009. "Market acceptance of cloud computing: An analysis of market structure, price models and service requirements," Bayreuth Reports on Information Systems Management 42, University of Bayreuth, Chair of Information Systems Management.
    3. Wen-Chyuan Chiang & Jason C.H. Chen & Xiaojing Xu, 2007. "An overview of research on revenue management: current issues and future research," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 1(1), pages 97-128.
    4. Mohammad Mahdi Kashef & Jorn Altmann, 2011. "A Cost Model for Hybrid Clouds," TEMEP Discussion Papers 201182, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Dec 2011.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Cloud Computing; Revenue Management; Pricing Strategy; Autonomic Resource Management.;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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