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Pricing Schemes in Cloud Computing: Utilization‐Based vs. Reservation‐Based

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  • Shi Chen
  • Hau Lee
  • Kamran Moinzadeh

Abstract

Cloud computing has been a rising trend in the business world. In this study, we consider two most important pricing schemes offered to sustained customers by major service providers in the cloud industry: the reservation‐based scheme (the R‐scheme) by Amazon or Microsoft, and the utilization‐based scheme (the U‐scheme) by Google. We consider a duopoly model with heterogeneous customers characterized by the mean and the coefficient of variation of their usage. We show that under either pricing scheme, the effective price is essentially an increasing function of the coefficient of variation of usage, and thus both schemes aim for rewarding stability in usage. However, when the providers adopt different schemes, we show that customers with lower demand volatility would prefer the R‐scheme, while those with higher demand volatility would prefer the U‐scheme. Furthermore, we study the impact of evolving market characteristics, including the distributions of market preference, demand size, and demand volatility, as well as the impact of the providers’ service levels on their choices of schemes and decisions on the pricing parameters. We find that if the market has a stronger preference for a particular provider or that provider has a higher service level than its competitor, the provider is more likely to adopt the R‐scheme, while its competitor's adoption of a scheme depends on the extent of the price competition. Specifically, when the diversity of customer preference becomes higher (lower), the price competition becomes softened (intensified), and the competitor is more likely to adopt the R‐scheme (U‐scheme, respectively).

Suggested Citation

  • Shi Chen & Hau Lee & Kamran Moinzadeh, 2019. "Pricing Schemes in Cloud Computing: Utilization‐Based vs. Reservation‐Based," Production and Operations Management, Production and Operations Management Society, vol. 28(1), pages 82-102, January.
  • Handle: RePEc:bla:popmgt:v:28:y:2019:i:1:p:82-102
    DOI: 10.1111/poms.12893
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    Citations

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    Cited by:

    1. Bo Li & Subodha Kumar, 2022. "Managing Software‐as‐a‐Service: Pricing and operations," Production and Operations Management, Production and Operations Management Society, vol. 31(6), pages 2588-2608, June.
    2. Chen, Claire Y.T. & Sun, Edward W. & Miao, Wanyu & Lin, Yi-Bing, 2024. "Reconciling business analytics with graphically initialized subspace clustering for optimal nonlinear pricing," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1086-1107.
    3. Shi Chen & Kamran Moinzadeh & Yong Tan, 2021. "Discount Schemes for the Preemptible Service of a Cloud Platform with Unutilized Capacity," Information Systems Research, INFORMS, vol. 32(3), pages 967-986, September.
    4. Lan Lu & Zheng Zhu & Pengfei Guo & Qiao‐Chu He, 2022. "Service Operations for Mixed Autonomous Paradigm: Lane Design and Subsidy," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1595-1612, April.
    5. Doan, Xuan Vinh & Lei, Xiao & Shen, Siqian, 2020. "Pricing of reusable resources under ambiguous distributions of demand and service time with emerging applications," European Journal of Operational Research, Elsevier, vol. 282(1), pages 235-251.
    6. Rajib L. Saha & Sumanta Singha & Subodha Kumar, 2021. "Does Congestion Always Hurt? Managing Discount Under Congestion in a Game-Theoretic Setting," Information Systems Research, INFORMS, vol. 32(4), pages 1347-1367, December.
    7. Manuel A. Nunez & Xue Bai & Linna Du, 2021. "Leveraging Slack Capacity in IaaS Contract Cloud Services," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 883-901, April.

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