Advertisement

Capacitated Network Revenue Management through Shadow Pricing

  • Mustapha Bouhtou
  • Madiagne Diallo
  • Laura Wynter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2816)

Abstract

In this paper, we analyze a method that links Lagrange multipliers from a resource allocation problem to the problem of revenue or profit maximization. This technique, first proposed in the transportation science literature by [7] has important implications for telecommunication network pricing. Indeed, the framework provides a generalization of telecommunication resource allocation/shadow price-based schemes such as those of [6] and [9], in that it permits the optimization of the shadow prices themselves, through a computationally simple procedure. We analyze the extent to which revenue can be maximized on a network that uses shadow-price-based prices, and how to deal with cases of unbounded multipliers.

Keywords

Internet Pricing Network Equilibrium Revenue Maximization Proportional Fairness Bilevel Program congestion control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bouhtou, M., Diallo, M., Wynter, L.: Fair network ressource allocation and link pricing: A numerical study. Technical Report RR-4680, INRIA-Rocquencourt (December 2002) Google Scholar
  2. 2.
    Cocchi, R., et al.: Pricing in computer networks: Motivation, formulation, and example. ACM/IEEE Trans. Net 1, 614–627 (1993), available from ftp://ftp.parc.xerox.eom/pub/net-research/pricingl.ps.Z CrossRefGoogle Scholar
  3. 3.
    Falkner, M., Devetsikiotis, M., Lambadaris, I.: An overwiew of pricing concepts for broadband ip networks. IEEE Communications Surveys and Tutorials 3(2) (Second Quarter 2000), available from: http://www.comsoc.org/pubs/surveys/
  4. 4.
    Fishburn, P.C., Odlyzko, A.M.: Dynamic behavior of differential pricing and quality of service options for the internet. In: Proc. First Intern. Conf. on Information and Computation Economies (ICE 1998), pp. 128–139. ACM Press, New York (1998); Extended version in Decision Support Systems, 28, 123-136 (2000), available from http://www.research.att.com/~amo CrossRefGoogle Scholar
  5. 5.
    Gupta, A., et al.: Priority pricing of integrated services networks. In: McK-night, L.W., Bailey, J.P. (eds.) Internet Economics, Cambridge, Massachusetts, pp. 323–352. MIT Press, Cambridge (1997)Google Scholar
  6. 6.
    Kelly, F.P.: Charging and rate control for elastic traffic. European Trans. Telecommunications 8, 33–37 (1997), available from http://www.statslab.cam.ac.uk/~frank/elastic.html CrossRefGoogle Scholar
  7. 7.
    Larsson, T., Patriksson, M.: Side constrained traffic equilibrium models— traffic management through link tolls. In: Marcotte, P., Nguyen, S. (eds.) Equilibrium and Advanced Transportation Modelling, Boston, MA, ch. 7, pp. 125–151. Kluwer Academic Publishers, Dordrecht (1998), available from the http://www.math.chalmers.se/~mipat/traffic.html Google Scholar
  8. 8.
    Scilab Scientific Computing Library, www-rocq.inria.fr/scilab/
  9. 9.
    Low, S.H., Lapsley, D.E.: Optimization flow control, 1: Basic algorithm and convergence. IEEE/ACM Transactions on Networking 7(6), 861–875 (1999)CrossRefGoogle Scholar
  10. 10.
    Mackie-Mason, J.K., Varian, H.R.: Pricing congestible network resources. IEEE JSAC 13(7), 1141–1149 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Mustapha Bouhtou
    • 1
  • Madiagne Diallo
    • 2
  • Laura Wynter
    • 3
  1. 1.France Telecom R&D, DAC/OATIssy-Les-MoulineauxFrance
  2. 2.Laboratoire PRiSMUniversité de VersaillesVersailles-CedexFrance
  3. 3.IBM Watson Research CenterYorktown HeightsUSA

Personalised recommendations