Individualized Pricing for a Cloud Provider Hosting Interactive Applications

Hossein Jahandideh (), Julie Ward Drew (), Filippo Balestrieri () and Kevin McCardle ()
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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

Service Science, 2020, vol. 12, issue 4, 130-147

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.

Keywords: cloud computing services; interactive applications; individualized pricing; approximate dynamic programming; stochastic dynamic programming (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orserv:v:12:y:2020:i:4:p:130-147

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