IDEAS home Printed from https://ideas.repec.org/a/inm/orserv/v12y2020i4p130-147.html
   My bibliography  Save this article

Individualized Pricing for a Cloud Provider Hosting Interactive Applications

Author

Listed:
  • Hossein Jahandideh

    (Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

  • Julie Ward Drew

    (Facebook Inc., Menlo Park, California 94025)

  • Filippo Balestrieri

    (Analysis Group, Inc., Boston, Massachusetts, 02199)

  • Kevin McCardle

    (Analysis Group, Inc., Boston, Massachusetts, 02199)

Abstract

We consider a cloud provider that hosts interactive applications, such as mobile apps and online games. Depending on the traffic of users for an application, the provider commits a subset of its resources (hardware capacity) to serve the application. The provider must choose a dynamic pricing mechanism to indirectly select the applications hosted and maximize revenue. We model the provider’s pricing problem as a large-scale stochastic dynamic program. To approach this problem, we propose a tractable approach to enable decomposing the multidimensional stochastic dynamic program into single-dimensional subproblems. We then extend the proposed framework to define an individualized dynamic pricing mechanism for the cloud provider. We present novel upper bounds on the optimal revenue to evaluate the performance of our pricing mechanism. The computational results show that a contract-based model of selling interactive cloud services achieves significantly greater revenue than the prevalent alternative and that our pricing scheme attains near-optimal revenue.

Suggested Citation

  • Hossein Jahandideh & Julie Ward Drew & Filippo Balestrieri & Kevin McCardle, 2020. "Individualized Pricing for a Cloud Provider Hosting Interactive Applications," Service Science, INFORMS, vol. 12(4), pages 130-147, December.
  • Handle: RePEc:inm:orserv:v:12:y:2020:i:4:p:130-147
    DOI: 10.1287/serv.2020.0264
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/serv.2020.0264
    Download Restriction: no

    File URL: https://libkey.io/10.1287/serv.2020.0264?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Burkart, Wolfgang R. & Klein, Robert & Mayer, Stefan, 2012. "Product line pricing for services with capacity constraints and dynamic substitution," European Journal of Operational Research, Elsevier, vol. 219(2), pages 347-359.
    2. Samuel Nathan Kirshner & Mikhail Nediak, 2015. "Scalable Dynamic Bid Prices for Network Revenue Management in Continuous Time," Production and Operations Management, Production and Operations Management Society, vol. 24(10), pages 1621-1635, October.
    3. David B. Brown & James E. Smith, 2014. "Information Relaxations, Duality, and Convex Stochastic Dynamic Programs," Operations Research, INFORMS, vol. 62(6), pages 1394-1415, December.
    4. Huseyin Topaloglu, 2009. "Using Lagrangian Relaxation to Compute Capacity-Dependent Bid Prices in Network Revenue Management," Operations Research, INFORMS, vol. 57(3), pages 637-649, June.
    5. Kumar, Subodha & Dutta, Kaushik & Mookerjee, Vijay, 2009. "Maximizing business value by optimal assignment of jobs to resources in grid computing," European Journal of Operational Research, Elsevier, vol. 194(3), pages 856-872, May.
    6. Mayer, Stefan & Steinhardt, Claudius, 2016. "Optimal product line pricing in the presence of budget-constrained consumers," European Journal of Operational Research, Elsevier, vol. 248(1), pages 219-233.
    7. Srikanth Jagabathula & Paat Rusmevichientong, 2017. "Nonparametric Joint Assortment and Price Choice Model," Management Science, INFORMS, vol. 63(9), pages 3128-3145, September.
    8. Moon, Ilkyeong & Park, Kun Soo & Hao, Jing & Kim, Dongwook, 2017. "Joint decisions on product line selection, purchasing, and pricing," European Journal of Operational Research, Elsevier, vol. 262(1), pages 207-216.
    9. Kalyan Talluri & Garrett van Ryzin, 1998. "An Analysis of Bid-Price Controls for Network Revenue Management," Management Science, INFORMS, vol. 44(11-Part-1), pages 1577-1593, November.
    10. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    11. Amir Fazli & Amin Sayedi & Jeffrey D. Shulman, 2018. "The Effects of Autoscaling in Cloud Computing," Management Science, INFORMS, vol. 64(11), pages 5149-5163, November.
    12. Kraus, Ursula G. & Yano, Candace Arai, 2003. "Product line selection and pricing under a share-of-surplus choice model," European Journal of Operational Research, Elsevier, vol. 150(3), pages 653-671, November.
    13. Daniel Adelman, 2007. "Dynamic Bid Prices in Revenue Management," Operations Research, INFORMS, vol. 55(4), pages 647-661, August.
    14. Dimitris Bertsimas & Ioana Popescu, 2003. "Revenue Management in a Dynamic Network Environment," Transportation Science, INFORMS, vol. 37(3), pages 257-277, August.
    15. Christian Borgs & Ozan Candogan & Jennifer Chayes & Ilan Lobel & Hamid Nazerzadeh, 2014. "Optimal Multiperiod Pricing with Service Guarantees," Management Science, INFORMS, vol. 60(7), pages 1792-1811, July.
    16. Ilyas Iyoob & Emrah Zarifoglu & A. B. Dieker, 2013. "Cloud Computing Operations Research," Service Science, INFORMS, vol. 5(2), pages 88-101, June.
    17. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    18. Sumit Kunnumkal & Kalyan Talluri, 2011. "Equivalence of Piecewise-Linear Approximation and Lagrangian Relaxation for Network Revenue Management," Working Papers 608, Barcelona School of Economics.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yuhang Ma & Paat Rusmevichientong & Mika Sumida & Huseyin Topaloglu, 2020. "An Approximation Algorithm for Network Revenue Management Under Nonstationary Arrivals," Operations Research, INFORMS, vol. 68(3), pages 834-855, May.
    2. Thomas W. M. Vossen & Dan Zhang, 2015. "Reductions of Approximate Linear Programs for Network Revenue Management," Operations Research, INFORMS, vol. 63(6), pages 1352-1371, December.
    3. Mika Sumida & Huseyin Topaloglu, 2019. "An Approximation Algorithm for Capacity Allocation Over a Single Flight Leg with Fare-Locking," INFORMS Journal on Computing, INFORMS, vol. 31(1), pages 83-99, February.
    4. Wang, Tingsong & Meng, Qiang & Tian, Xuecheng, 2024. "Dynamic container slot allocation for a liner shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    5. Chevalier, Philippe & Lamas, Alejandro & Lu, Liang & Mlinar, Tanja, 2015. "Revenue management for operations with urgent orders," European Journal of Operational Research, Elsevier, vol. 240(2), pages 476-487.
    6. Georgia Perakis & Guillaume Roels, 2010. "Robust Controls for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 12(1), pages 56-76, November.
    7. Wuyang Yuan & Lei Nie & Xin Wu & Huiling Fu, 2018. "A dynamic bid price approach for the seat inventory control problem in railway networks with consideration of passenger transfer," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-23, August.
    8. Nicolas Houy & François Le Grand, 2015. "The Monte Carlo first-come-first-served heuristic for network revenue management," Working Papers halshs-01155698, HAL.
    9. Chaoxu Tong & Huseyin Topaloglu, 2014. "On the Approximate Linear Programming Approach for Network Revenue Management Problems," INFORMS Journal on Computing, INFORMS, vol. 26(1), pages 121-134, February.
    10. Dan Zhang, 2011. "An Improved Dynamic Programming Decomposition Approach for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 13(1), pages 35-52, April.
    11. Meissner, Joern & Strauss, Arne, 2012. "Improved bid prices for choice-based network revenue management," European Journal of Operational Research, Elsevier, vol. 217(2), pages 417-427.
    12. Nicolas Houy & François Le Grand, 2015. "Financing and advising with (over)confident entrepreneurs : an experimental investigation," Working Papers 1514, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    13. Alexander Erdelyi & Huseyin Topaloglu, 2010. "A Dynamic Programming Decomposition Method for Making Overbooking Decisions Over an Airline Network," INFORMS Journal on Computing, INFORMS, vol. 22(3), pages 443-456, August.
    14. Juan M. Chaneton & Gustavo Vulcano, 2011. "Computing Bid Prices for Revenue Management Under Customer Choice Behavior," Manufacturing & Service Operations Management, INFORMS, vol. 13(4), pages 452-470, October.
    15. Yuri Levin & Mikhail Nediak & Huseyin Topaloglu, 2012. "Cargo Capacity Management with Allotments and Spot Market Demand," Operations Research, INFORMS, vol. 60(2), pages 351-365, April.
    16. Syed Asif Raza & Rafi Ashrafi & Ali Akgunduz, 2020. "A bibliometric analysis of revenue management in airline industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(6), pages 436-465, December.
    17. Ş. İlker Birbil & J. B. G. Frenk & Joaquim A. S. Gromicho & Shuzhong Zhang, 2014. "A Network Airline Revenue Management Framework Based on Decomposition by Origins and Destinations," Transportation Science, INFORMS, vol. 48(3), pages 313-333, August.
    18. Dan Zhang & Zhaosong Lu, 2013. "Assessing the Value of Dynamic Pricing in Network Revenue Management," INFORMS Journal on Computing, INFORMS, vol. 25(1), pages 102-115, February.
    19. Dan Zhang & Larry Weatherford, 2017. "Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 18-35, February.
    20. David Sayah, 2015. "Approximate Linear Programming in Network Revenue Management with Multiple Modes," Working Papers 1518, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orserv:v:12:y:2020:i:4:p:130-147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.