Competitive Pricing for Cloud Information Resources

Authors

  • Pavel Zakharov Saint Petersburg State University

Abstract

This article explores a pricing model for cloud resources, based on use of two different payment schemes - reservation and pay-as-you-go, each of which is controlled by its administrator. The process of prices determination has a form of a two-stage game. At the first stage, administrators set prices for their cloud resources, trying to maximize their revenue. At this stage, a static non-cooperative two-person game is solved, where administrators act as players; their strategies are the prices for resources; their utilities depend both on prices and on the number of resources sold. At the second stage, with prices values given, customers choose a scheme of payment. Making a choice they seek to minimize their expected costs, which consist of the financial component and the waiting costs. First Wardrop principle is used in order to describe user behaviour and optimality conditions in the second stage of the game. The analysis of the solutions obtained shows the economic efficiency of an additional payment scheme. The numerical examples show, that the utility of the reservation scheme administrator is higher than that of the pay-as-you-go scheme.

Keywords:

pricing, cloud resources, two-stage non-cooperative game, Nash equilibrium

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References

Al-Roomi, M., S. Al-Ebrahim, S. Buqrais and I. Ahmad (2013). Cloud Computing Pricing Models: A Survey. International Journal of Grid and Distributed Computing, 6, 93–106.

Anselmi, U., S. Ayesta and A. Wierman (November 2011). Competition yields efficiency in load balancing games. Performance Evaluation, 68(11), 986–1001.

Ben-Yehuda, O.A., M. Ben-Yehuda, A. Schuster and D. Tsafrir (2011). Deconstructing Amazon EC2 Spot Instance Pricing. IEEE Third International Conference on Cloud Computing Technology and Science, Athens, 304–311.

Boyd, S. and L. Vandenberghe (2004). Convex Optimization, Cambridge University Press.

Calheiros, R.N., R. Ranjan and R. Buyya (2011). Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments. International Conference on Parallel Processing, Taipei City, 295–304.

Cuong, T., H. Nguyen, E.-N. Huh, C.S. Hong, D. Niyato and Z. Han (2016). Dynamics of service selection and provider pricing game in heterogeneous cloud market. Journal of Network and Computer Applications, 69, 152–165.

Feng, Y., B. Li and B. Li (Jan. 2014). Price Competition in an Oligopoly Market with Multiple IaaS Cloud Providers. IEEE Transactions on Computers, 63(1), 59–73.

Ferreira, M.A.M. (2015). Networks of Queues Models with Several Classes of Customers and Exponential Service Times. Applied Mathematical Sciences, 9, 3789–3796.

Hadji, M., W. Louati and D. Zeghlache (2011). Constrained Pricing for Cloud Resource Allocation. IEEE 10th International Symposium on Network Computing and Applica¬tions, Cambridge, MA, 359–365.

Introna, D.L. (1991). The Impact of Information Technology on Logistics. International Journal of Physical Distribution & Logistics Management, 21, 32–37.

Künsemöller, J. and H. Karl (2012). A Game-Theoretical Approach to the Benefits of Cloud Computing. In: Economics of Grids, Clouds, Systems, and Services. GECON 2011. Lecture Notes in Computer Science (Vanmechelen K., Altmann J., Rana O.F., eds), Vol. 7150, pp. 148-160. Springer, Berlin, Heidelberg.

Li, С., X. Zhang and L. Li (2014). Research on Comparative Analysis of Regional Logistics Information Platform Operation Mode Based on Cloud Computing. International Journal of Future Generation Communication and Networking, 7(2), 73–80.

Mazzucco, M. and M. Dumas (2011). Reserved or On-Demand Instances? A Revenue Maximization Model for Cloud Providers. IEEE 4th International Conference on Cloud Computing, Washington, DC, 428–435.

Niu, D., C. Feng and B. Li (2012). Pricing cloud bandwidth reservations under demand uncertainty. Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems (SIGMETRICS ’12). ACM, New York, NY, USA, 151–162.

Osborne, M.J. and A. Rubinstein (1994). A Course in Game Theory, MIT Press, Cambridge, Mass.

Petrosian, L.A., N.A. Zenkevich and E.V. Shevkoplyas (2012). Game Theory. Saint-Petersburg: BHV-Petersburg (in Russian).

Sun, G., X.-Y. Wang, H. Wang and J. Zhao (2015). Construction of Regional Logistics Information Platform Based on Cloud Computing . International Conference on Computational Science and Engineering, Atlantis Press.

Sheffi, Y. (1985). Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice-Hall, Inc., Englewood Cliffs, N.J. 07632.

Sztrik, J. (2012). Basic Queueing Theory, University of Debrecen Faculty of Informatics.

Urgaonkar, B., G. Kesidis, U.V. Shanbhag and C. Wang (2013). Pricing of service in clouds: optimal response and strategic interactions. SIGMETRICS Performance Evaluation Review, 41(3), 28–30

Wang, W., D. Niu, B. Liang and B. Li (2015). Dynamic Cloud Instance Acquisition via IaaS Cloud Brokerage. IEEE Transactions on Parallel and Distributed Systems, 26(6), 1580–1593

Wardrop, J.G. (1952). Road paper. Some theoretical aspects of road traffic research. Proceedings of the Institution of Civil Engineers, 1(3), 325–362 Part 1.

Xu, H. and B. Li (2013). A study of pricing for cloud resources. ACM SIGMETRICS Performance Evaluation Review, 40(4), 3–12.

Zhang, S., H. Yan and X. Chen (2012). Research on Key Technologies of Cloud Computing. Physics Procedia 33, 1791–1797.

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Published

2022-03-08

How to Cite

Zakharov, P. (2022). Competitive Pricing for Cloud Information Resources. Contributions to Game Theory and Management, 12. Retrieved from https://gametheory.spbu.ru/article/view/13034

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