Automation and Remote Control

, Volume 79, Issue 4, pp 737–756 | Cite as

A Game-Theoretic Model of Virtual Operators Competition in a Two-Sided Telecommunication Market

  • V. V. Mazalov
  • Yu. V. Chirkova
  • J. Zheng
  • J. W. Lien
Mathematical Game Theory and Applications


This paper considers a market where two large companies provide services to the population through “cloud” virtual operators buying companies’ services and reselling them to clients. Each large company assigns a price for selling its services to virtual operators. Also the number of its clients and its resource (a characteristic of company’s attractiveness for clients) are known. The game process is a repetition of two-step games where virtual operators choose companies and prices for their services. Each virtual operator needs to choose a company whose services he is going to sell and also to define a price for the services to be sold to clients. Each virtual operator establishes the probability to choose the company and the price for services, taking into account that the partition of company’s clients choosing a given operator is defined by the Hotelling specification. At each step, each virtual operator seeks to maximize his payoff. We find the optimal strategies of the virtual operators and also explore the following question. Does the system achieve some stationary state in this repeated two-step game or a repeating cycle of states is formed instead?


cloud operators repeated two-step game Hotelling specification Nash equilibrium stationary state 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Armstrong, M., Competition in Two-Sided Markets, RAND J. Economics, 2006, vol. 37, no. 3, pp. 668–691.CrossRefGoogle Scholar
  2. 2.
    Chang, F., Ren, J., and Viswanathan, R., Optimal Resource Allocation in Clouds, Proc. 3rd Int. Conf. on Cloud Computing, Cloud 2010, Washington: IEEE Computer Society, 2010, pp. 418–425.Google Scholar
  3. 3.
    Chaisiri, S., Lee, B.-S., and Niyato, D., Optimization of Resource Provisioning Cost in Cloud Computing, IEEE Trans. Services Comput., 2012, vol. 5 (2), pp. 164–177.CrossRefGoogle Scholar
  4. 4.
    Karakitsiou, A. and Migdalas, A., Locating Facilities in a Competitive Environment, Optimiz. Lett., 2017, vol. 11 (5), pp. 929–945.MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Kiiski, A. and Hämmäinen, H., Mobile Virtual Network Operator Strategies: Case Finland, ITS 15th Biennial Conf., Berlin, 2004. MVNO.pdfGoogle Scholar
  6. 6.
    Kllapi, H., Sitaridi, E., Tsangaris, M.M., and Ioannidis, Y., Schedule Optimization for Data Processing Ows on the Cloud, Proc. 2011 ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’11), New York: ACM, 2011, pp. 289–300. Scholar
  7. 7.
    Mazalov, V.V., Mathematical Game Theory and Applications, New York: Wiley, 2014.zbMATHGoogle Scholar
  8. 8.
    Mazalov, V., Lukyanenko, A., and Luukkainen, S., Equilibrium in Cloud Computing Market, Perform. Evaluat., 2015, vol. 92, pp. 40–50.CrossRefGoogle Scholar
  9. 9.
    Mazalov, V.V. and Melnik, A.V., Equilibrium Prices and Flows in the Passenger Traffic Problem, Int. Game Theory Rev., 2016, vol. 18, no.1.Google Scholar
  10. 10.
    Raivio, Y., Mazhelis, O., Annapureddy, K., Mallavarapu, R., and Tyrväinen, P., Hybrid Cloud Architecture for Short Message Services, Proc. 2nd Int. Conf. on Cloud Computing and Services Science, Closer: SciTePress, 2012, pp. 489–500.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  • V. V. Mazalov
    • 1
  • Yu. V. Chirkova
    • 1
  • J. Zheng
    • 3
  • J. W. Lien
    • 2
  1. 1.Institute of Applied Mathematical Research, Karelian Research CenterRussian Academy of SciencesPetrozavodskRussia
  2. 2.Department of Decision Sciences and Managerial EconomicsThe Chinese University of Hong Kong, ShatinHong KongChina
  3. 3.School of Economics and ManagementTsinghua UniversityBeijingChina

Personalised recommendations