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dimensioning, which formulates bandwidth dimensioning at egress links to peering/transit networks taking into account measured core network traffic conditions. The work discussed in [1] has similar objectives to our research. However, the service model considered in [1, 3] differs from our proposed model and traffic statistics are not taken into account in the allocation procedure. Moreover, in our work we suggest a distributed architecture implementation, while in these works only a centralized scheme is considered. A policy-based architecture is presented in [4], where a measurement-based approach is proposed for dynamic Quality of Service adaptation in DiffServ networks. The proposed architecture is composed of one Policy Decision Point (PDP), a set of Policy Enforcement Points that are installed in ingress routers and bandwidth monitors implemented in core routers. When monitors detect significant changes in available bandwidth they inform the PDP which changes dynamically the policies on in-profile and out-of-profile input traffics based on the current state of the network estimated using the information collected by the monitors. However, this scheme, while achieving dynamic QoS adaptation for multimedia applications, does not take into account the users utility function and their eventual willingness to be charged for transmitting out-of-profile traffic, thus increasing network revenue. In [5], the authors propose an active resource management approach (ARM) for a differentiated services environment. The basic concept behind ARM is that by effectively knowing when a client is sending packets and how much of its allocated bandwidth is being used at any given time, the unused bandwidth can be reallocated without loss of service. This concept is in line with our research objectives. Differently from our work, however, ARM does not guarantee to the user a minimum subscribed bandwidth throughout the contract duration since unused bandwidth is sent to a pool of available bandwidth and it can be used to admit new connections in the network, in spite of those already admitted. As we have mentioned previously, admission control mechanisms are complementary to resource provisioning algorithms for guaranteeing quality of service to network users. 30
Shenker in [10] states that the network service model can be augmented with admission control, which is the ability to turn some flows away when the network is overloaded. In other words, the network should turn away additional flows when admitting them would lead to violating its quantitative service commitments. Therefore, in our work, we assume the existence of admission control algorithms at the edge [20, 21, 22] of the network that cooperate with our proposed bandwidth allocation algorithms operating inside the network. In the following, we present a brief review of two classes of admission control algorithms proposed in the literature. In [20], the authors propose an endpoint admission control scheme which avoids the complexities of per-flow state in network routers. The basic idea is that the end hosts (or edge routers) probe the network by sending packets at the rate that a new flow would like to reserve. The flow is admitted if the resulting loss rate of probing packets is sufficiently low. Alternatively, the admission control test can be performed based on available bandwidth measurements, avoiding the negative effect of losses. The scheme described in [21] is part of a more general admission control scheme that bases pricing on ECN (Explicit Congestion Notification) marks. All packets are treated identically: data and probe packets are indistinguishable, as are best-effort and real-time packets, and they are marked upon congestion. Flows can send as much traffic as they wish, but must pay for those packets that are marked. In this setting, admission control is a service offered by third-parties (or the network itself) to provide a guaranteed price for the flow (as opposed to guaranteeing the level of service, in traditional IntServ). 31
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 and 36: Utility Function Types In a multi-a
Page 37 and 38: comes smaller than the application
Page 39 and 40: of which there exist extensive theo
Page 41: allows us to allocate the bandwidth
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 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
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Page 85 and 86: Table 7.3: Peak rate, subscribed ra
Page 87 and 88: Chapter 8 Performance Upper Bound T
Page 89 and 90: Table 8.1: Average performance gain
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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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