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A Market-Based Approach to Optimal Resource Allocation in Integrated-Services Connection-Oriented Networks

Author

Listed:
  • Panagiotis Thomas

    (Advanced Network Technology, Motorola, Inc., Arlington Heights, Illinois 60004)

  • Demosthenis Teneketzis

    (Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109)

  • Jeffrey K. Mackie-Mason

    (Department of Economics and School of Information, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

We present an approach to the admission control and resource allocation problem in connection-oriented networks that offer multiple services to users. users' preferences are summarized by means of their utility functions, and each user is allowed to request more than one type of service. Multiple types of resources are allocated at each link along the path of a connection. We assume that the relation between Quality of Service (QoS) and resource allocation is given, and we incorporate it as a constraint into a static optimization problem. The objective of the optimization problem is to determine the amount of and required resources for each type of service to maximize the sum of the users' utilities. We prove the existence of a solution of the optimization problem and describe a competitive market economy that implements the solution and satisfies the informational constraints imposed by the nature of the decentralized resource allocation problem. The economy consists of four different types of agents: resource providers, service providers, users, and an auctioneer that regulates the prices based on the observed aggregate excess demand. The goods that are sold are: (i) the resources at each link of the network, and (ii) services constructed from these resources and then delivered to users. We specify an iterative procedure that is used by the auctioneer to update the prices, and we show that it leads to an allocation that is arbitrarily close to a solution of the optimization problem in a finite number of iterations.

Suggested Citation

  • Panagiotis Thomas & Demosthenis Teneketzis & Jeffrey K. Mackie-Mason, 2002. "A Market-Based Approach to Optimal Resource Allocation in Integrated-Services Connection-Oriented Networks," Operations Research, INFORMS, vol. 50(4), pages 603-616, August.
    • Handle: RePEc:inm:oropre:v:50:y:2002:i:4:p:603-616
    DOI: 10.1287/opre.50.4.603.2862
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    References listed on IDEAS

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    1. Gupta, Alok & Stahl, Dale O. & Whinston, Andrew B., 1997. "A stochastic equilibrium model of internet pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 21(4-5), pages 697-722, May.
    2. Hurwicz, Leonid, 1973. "The Design of Mechanisms for Resource Allocation," American Economic Review, American Economic Association, vol. 63(2), pages 1-30, May.
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    Cited by:

    1. christoph Engel, 2005. "Voice over IP. Competition Policy and Regulation," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2005_26, Max Planck Institute for Research on Collective Goods.
    2. Minghui Lai & Weili Xue & Qian Hu, 2019. "An Ascending Auction for Freight Forwarder Collaboration in Capacity Sharing," Transportation Science, INFORMS, vol. 53(4), pages 1175-1195, July.
    3. Zhiling Guo & Gary J. Koehler & Andrew B. Whinston, 2012. "A Computational Analysis of Bundle Trading Markets Design for Distributed Resource Allocation," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 823-843, September.
    4. Zhongju Zhang & Debabrata Dey & Yong Tan, 2007. "Pricing Communication Services with Delay Guarantee," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 248-260, May.
    5. Engel, Christoph, 0. "Competition in a pure world of Internet telephony," Telecommunications Policy, Elsevier, vol. 31(8-9), pages 530-540, September.
    6. Panos Parpas & Berç Rustem, 2008. "A Pricing Mechanism for Resource Management in Grid Computing," Computational Economics, Springer;Society for Computational Economics, vol. 31(4), pages 381-395, May.
    7. 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.
    8. Pavlin, J. Michael, 2017. "Dual bounds of a service level assignment problem with applications to efficient pricing," European Journal of Operational Research, Elsevier, vol. 262(1), pages 239-250.
    9. Tudor Stoenescu & Mingyan Liu & Demosthenis Teneketzis, 2007. "Multirate multicast service provisioning II: a tâtonnement process for rate allocation," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(3), pages 389-415, June.
    10. Tudor Stoenescu & Mingyan Liu & Demosthenis Teneketzis, 2007. "Multirate multicast service provisioning I: an algorithm for optimal price splitting along multicast trees," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(2), pages 199-228, April.
    11. Joakim Kalvenes & Neil Keon, 2007. "Traffic Estimation and Capacity Assignment in Multimedia Distribution Networks with Guaranteed Quality of Service," Operations Research, INFORMS, vol. 55(3), pages 518-531, June.
    12. Cui, Geng & Wong, Man Leung & Wan, Xiang, 2015. "Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 27-37.

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