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[26] P. Reichl, S. Leinen, and B. Stiller, “A practical review of pricing and cost recovery for Internet services,” in Proceedings of the 2nd Berlin Internet Economics Workshop (IEW’99), Berlin, Germany, May 1999. [27] L. Massoulié and J. Roberts, “Bandwidth sharing: objectives and algorithms,” IEEE/ACM Transactions on Networking, pp. 320–328, vol. 10, no. 3, June 2002. [28] S. H. Low and D. E. Lapsley, “Optimization flow control-I: basic algorithm and conver- gence,” IEEE/ACM Transactions on Networking, pp. 861–874, vol. 7, no. 6, December 1999. [29] S. Athuraliya and S. H. Low, “Optimization flow control-II: Implementation,” in Pro- ceedings of IEEE INFOCOM, 2000. [30] K. Kar, S. Sarkar, and L. Tassiulas, “A simple rate control algorithm for maximizing total user utility,” IEEE INFOCOM, pp. 133–141, 2001. [31] H. Yaïche, R. R. Mazumdar, and C. Rosenberg, “A game theoretic framework for bandwidth allocation and pricing of elastic connections in broadband networks: theory and algorithms,” IEEE/ACM Transactions on Networking, pp. 667–678, vol. 8, no. 5, October 2000. [32] S. Kunniyur and R. Srikant, “End-to-end congestion control schemes: utility function, random losses and ECN marks,” IEEE INFOCOM, pp. 1323–1332, 2000. [33] A. Charny and K. K. Ramakrishnan, “Time Scale Analysis of Explicit Rate Allocation in ATM Networks,” in Proceedings of IEEE INFOCOM, April 1996. [34] J-Sim, “Available at www.j-sim.org.” Ohio State University. [35] R. Fourer, D. M. Gay, and B. W. Kernighan, AMPL: A Modeling Language for Math- ematical Programming. Duxbury Press/Brooks/Cole Publishing Company, 2002. 90
[36] K. Pawlikowski, H.-D. J. Jeong, and J.-S. R. Lee, “On credibility of Simulation Studies of Telecommunications Networks,” IEEE Communications Magazine, pp. 132–139, January, 2002. [37] N. Meskaoui, “A framework to model and simulate multi-agent systems within telecommunication networks: New environment, tools and behaviors.” P.H.D. Thesis report, Paris 6 University, France, 2004. 91
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Thèse de Doctorat de l’universit
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To my family.
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Abstract Efficient dynamic resource
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Acknowledgements I would like to th
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4.2 Connections Classification . .
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Résumé delaThèse 1. La probléma
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supérieure sur les performances qu
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compte les fonctions d’utilité d
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(Policy Decision Point), d’un ens
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ligne pour individualiser la bande
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6. Nous proposons trois algorithmes
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appelons débit de crête. Six sour
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6. Conclusion générale et Perspec
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Chapter 1 Introduction The Internet
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upper bounds to the performance ach
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Chapter 2 Dynamic Bandwidth Allocat
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Utility Function Types In a multi-a
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comes smaller than the application
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of which there exist extensive theo
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allows us to allocate the bandwidth
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Shenker in [10] states that the net
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implements such service model by pe
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considering different constraints l
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4.1 Problem Statement Let us model
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Page 63 and 64: Chapter 6 Mathematical Model This C
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Page 67 and 68: Chapter 7 Numerical Results The mai
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Page 87 and 88: Chapter 8 Performance Upper Bound T
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Page 95 and 96: Chapter 9 Conclusion 9.1 Discussion
Page 97: established by creating a session i
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Page 105 and 106: Appendix A Algorithm Implementation
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Page 113 and 114: Table A.2: Java code for updating t
Page 115 and 116: List of Acronyms ACA Autonomous Com
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Page 119 and 120: 8.1 Performance upper bound for all
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