Th`ese de Doctorat de l'université Paris VI Pierre et Marie Curie Mlle ...

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(a) Elastic Utility (b) Hard Real-time Utility (c) Delay-Adaptive Utility (d) Rate-Adaptive Utility Figure 2.1: Examples of bandwidth utility functions: (a) Elastic utility for data transfer applications, (b) Hard real-time utility for audio and video applications, (c) utility for delay-adaptive real-time applications and (d) utility for rate-adaptive real-time applications 24
comes smaller than the application transmission rate. For delay-adaptive audio and video applications, the utility function curves can look like that in Figure 2.1(c). Another class of real-time applications is represented by rate-adaptive applications. These applications do not require hard bandwidth guarantees and hence they can adapt their transmission rate in response to network congestion. The utility function for rate- adaptive applications has the shape shown in Figure 2.1(d). Pricing The pricing can represent an important component of bandwidth utility functions [2, 26]. Dynamic pricing mechanisms can take advantage of application adaptivity to increase network availability, revenue, and user-perceived utility [2]. In a network with QoS support, pricing of network services based on the level of service, usage, and congestion provides an incentive for applications to adapt their sending rates according to network conditions. Increasing the price during congestion gives the application an incentive to back-off its sending rate and at the same time allows an application with more stringent bandwidth and QoS requirements to maintain a high quality by paying more. A number of pricing schemes are used in the Internet today like access-rate-dependent charge, volume-dependent charge, or the combination of the both. These charging schemes are described in [26]. 2.2 Dynamic Bandwidth Allocation Algorithms 2.2.1 Max-Min Fair Allocation Algorithms Several algorithms have been proposed to allocate network resources fairly among com- peting connections. A max-min fair allocation algorithm is introduced in [13] to allocate 25
Page 1 and 2: Thèse de Doctorat de l’universit
Page 3 and 4: To my family.
Page 5 and 6: Abstract Efficient dynamic resource
Page 7: Acknowledgements I would like to th
Page 10 and 11: 4.2 Connections Classification . .
Page 13 and 14: Résumé delaThèse 1. La probléma
Page 15 and 16: supérieure sur les performances qu
Page 17 and 18: compte les fonctions d’utilité d
Page 19 and 20: (Policy Decision Point), d’un ens
Page 21 and 22: ligne pour individualiser la bande
Page 23 and 24: 6. Nous proposons trois algorithmes
Page 25 and 26: appelons débit de crête. Six sour
Page 27 and 28: 6. Conclusion générale et Perspec
Page 29 and 30: Chapter 1 Introduction The Internet
Page 31 and 32: upper bounds to the performance ach
Page 33 and 34: Chapter 2 Dynamic Bandwidth Allocat
Page 35: Utility Function Types In a multi-a
Page 39 and 40: of which there exist extensive theo
Page 41 and 42: allows us to allocate the bandwidth
Page 43: Shenker in [10] states that the net
Page 46 and 47: implements such service model by pe
Page 48 and 49: considering different constraints l
Page 50 and 51: 4.1 Problem Statement Let us model
Page 52 and 53: y all network users. Using the nota
Page 54 and 55: in the following Section. The struc
Page 56 and 57: Optimum Bandwidth Allocation Algori
Page 58 and 59: Table 5.1: Pseudo-code specificatio
Page 60 and 61: of all greedy connections that are
Page 63 and 64: Chapter 6 Mathematical Model This C
Page 65 and 66: 1 2 3 4 0000000000 1111111111 00000
Page 67 and 68: Chapter 7 Numerical Results The mai
Page 69 and 70: the two core nodes is equal to 6 Mb
Page 71 and 72: Total Accepted Load (Mb/s) 6 5.5 5
Page 73 and 74: Total Accepted Load (Mb/s) 6 5.5 5
Page 75 and 76: plottedinFigure7.9. Figures 7.10 an
Page 77 and 78: Total Accepted Load (Mb/s) 6 5 4 3
Page 79 and 80: S 2 S 3 S 4 1 S 6 S1 S5 2 3 4 5 D 1
Page 81 and 82: high network loads, where it achiev
Page 83 and 84: proposed allocation algorithms in a
Page 85 and 86: Table 7.3: Peak rate, subscribed ra
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Chapter 8 Performance Upper Bound T
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Table 8.1: Average performance gain
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Total Accepted Load (Mb/s) 7 6.5 6
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Total Accepted Load (Mb/s) 6 5 4 3
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Chapter 9 Conclusion 9.1 Discussion
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established by creating a session i
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[7] F. P. Kelly, “Charging and ra
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[26] P. Reichl, S. Leinen, and B. S
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Appendix A Algorithm Implementation
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these latters are implemented using
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• boolean State: the connection s
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(CSTtable) is updated by the ingres
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Table A.2: Java code for updating t
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List of Acronyms ACA Autonomous Com
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List of Figures 1 Notre architectur
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8.1 Performance upper bound for all
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List of Tables 5.1 Pseudo-code spec
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