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Revenue management for Cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty

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  • Püschel, Tim
  • Schryen, Guido
  • Hristova, Diana
  • Neumann, Dirk

Abstract

Cloud computing promises the flexible delivery of computing services in a pay-as-you-go manner. It allows customers to easily scale their infrastructure and save on the overall cost of operation. However Cloud service offerings can only thrive if customers are satisfied with service performance. Allowing instantaneous access and flexible scaling while maintaining the service levels and offering competitive prices poses a significant challenge to Cloud computing providers. Furthermore services will remain available in the long run only if this business generates a stable revenue stream. To address these challenges we introduce novel policy-based service admission control models that aim at maximizing the revenue of Cloud providers while taking informational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly outperform first come first serve approaches, which are still state of the art. Furthermore the results give insights in how and to what extent uncertainty has a negative impact on revenue.

Suggested Citation

  • Püschel, Tim & Schryen, Guido & Hristova, Diana & Neumann, Dirk, 2015. "Revenue management for Cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 637-647.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:2:p:637-647
    DOI: 10.1016/j.ejor.2015.01.027
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    References listed on IDEAS

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    Cited by:

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    2. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2018. "Co-residence based data vulnerability vs. security in cloud computing system with random server assignment," European Journal of Operational Research, Elsevier, vol. 267(2), pages 676-686.
    3. 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.
    4. Xiang Zhao & Xinghua Shan & Jinfei Wu, 2023. "The Impact of Seat Resource Fragmentation on Railway Network Revenue Management," Networks and Spatial Economics, Springer, vol. 23(1), pages 135-177, March.
    5. Chen, Li-Ming & Chang, Wei-Lun, 2020. "Under what conditions can an application service firm with in-house computing benefit from cloudbursting?," European Journal of Operational Research, Elsevier, vol. 282(1), pages 71-80.
    6. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    7. Pascual, Fanny & Rzadca, Krzysztof, 2018. "Colocating tasks in data centers using a side-effects performance model," European Journal of Operational Research, Elsevier, vol. 268(2), pages 450-462.

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