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Pricing and Resource Allocation in Caching Services with Multiple Levels of Quality of Service

Author

Listed:
  • Kartik Hosanagar

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Ramayya Krishnan

    (Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • John Chuang

    (University of California, Berkeley, California)

  • Vidyanand Choudhary

    (University of California, Irvine, California)

Abstract

Network caches are the storage centers in the supply chain for content delivery---the digital equivalent of warehouses. Operated by access networks and other operators, they provide benefits to content publishers in the forms of bandwidth cost reduction, response time improvement, and handling of flash crowds. Yet, caching has not been fully embraced by publishers, because its use can interfere with site personalization strategies and/or collection of visitor information for business intelligence purposes. While recent work has focused on technological solutions to these issues, this paper provides the first study of the managerial issues related to the design and provisioning of incentive-compatible caching services. Starting with a single class of caching service, we find conditions under which the profit-maximizing cache operator should offer the service for free. This occurs when the access networks' bandwidth costs are high and a large fraction of content publishers value personalization and business intelligence. Some publishers will still opt out of the service, i.e., cache bust, as observed in practice. We next derive the conditions under which the profit-maximizing cache operator should provision two vertically differentiated service classes, namely, premium and best effort. Interestingly, caching service differentiation is different from traditional vertical differentiation models, in that the premium and best-effort market segments do not abut. Thus, optimal prices for the two service classes can be set independently and cannibalization does not occur. It is possible for the cache operator to continue to offer the best-effort service for free while charging for the premium service. Furthermore, consumers are better off because more content is cached and delivered faster to them. Finally, we find that declining bandwidth costs will put negative pressure on cache operator profits, unless consumer adoption of broadband connectivity and the availability of multimedia content provide the necessary increase in traffic volume for the caches.

Suggested Citation

  • Kartik Hosanagar & Ramayya Krishnan & John Chuang & Vidyanand Choudhary, 2005. "Pricing and Resource Allocation in Caching Services with Multiple Levels of Quality of Service," Management Science, INFORMS, vol. 51(12), pages 1844-1859, December.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:12:p:1844-1859
    DOI: 10.1287/mnsc.1050.0420
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    References listed on IDEAS

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    Cited by:

    1. Mehran Garmehi & Morteza Analoui & Mukaddim Pathan & Rajkumar Buyya, 2015. "An economic mechanism for request routing and resource allocation in hybrid CDN–P2P networks," International Journal of Network Management, John Wiley & Sons, vol. 25(6), pages 375-393, November.
    2. Kartik Hosanagar & John Chuang & Ramayya Krishnan & Michael D. Smith, 2008. "Service Adoption and Pricing of Content Delivery Network (CDN) Services," Management Science, INFORMS, vol. 54(9), pages 1579-1593, September.
    3. Anindya Datta & Kaushik Dutta & Qianhui Liang & Debra VanderMeer, 2012. "SOA Performance Enhancement Through XML Fragment Caching," Information Systems Research, INFORMS, vol. 23(2), pages 505-535, June.
    4. Elias Vathias & Stathes Hadjiefthymiades, 2022. "A stock options metaphor for content delivery networks," Netnomics, Springer, vol. 22(2), pages 195-230, October.
    5. Elias Vathias & Eleftheria Katsarou & Stathes Hadjiefthymiades, 2017. "A secondary market metaphor for content delivery networks," Netnomics, Springer, vol. 18(2), pages 183-214, December.
    6. E. Borgonovo & L. Peccati, 2011. "Managerial insights from service industry models: a new scenario decomposition method," Annals of Operations Research, Springer, vol. 185(1), pages 161-179, May.
    7. Yang Yu & Ray Qing Cao & Dara Schniederjans, 2017. "Cloud computing and its impact on service level: a multi-agent simulation model," International Journal of Production Research, Taylor & Francis Journals, vol. 55(15), pages 4341-4353, August.
    8. De Liu & Jianqing Chen & Andrew B. Whinston, 2010. "Ex Ante Information and the Design of Keyword Auctions," Information Systems Research, INFORMS, vol. 21(1), pages 133-153, March.
    9. Kartik Hosanagar & Yong Tan, 2012. "Cooperative Cashing? An Economic Analysis of Document Duplication in Cooperative Web Caching," Information Systems Research, INFORMS, vol. 23(2), pages 356-375, June.
    10. Chia-Wei Kuo & Kwei-Long Huang & Chao-Lung Yang, 2017. "Optimal contract design for cloud computing service with resource service guarantee," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1030-1044, September.
    11. Hemant K. Bhargava & Vidyanand Choudhary, 2008. "Research Note--When Is Versioning Optimal for Information Goods?," Management Science, INFORMS, vol. 54(5), pages 1029-1035, May.
    12. Elias Vathias & Stathes Hadjiefthymiades, 2021. "A stock options metaphor for content delivery networks," Netnomics, Springer, vol. 22(2), pages 195-230, December.
    13. Abhijit Dutt & Hemant Jain & Sanjeev Kumar, 2018. "Providing Software as a Service: a design decision(s) model," Information Systems and e-Business Management, Springer, vol. 16(2), pages 327-356, May.

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