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

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6 Mb/s. In this situation, in fact, the average number of idle connections (i.e. 6) is suffi- ciently high to exalt our dynamic allocation algorithms that reallocate unused bandwidth to active users who can take advantage of it, sending extra-traffic and generating network extra-revenue. With lower Off time values (i.e. with higher offered loads) the total revenue slightly decreases as less connections are idle, in average, and consequently less bandwidth is available for re-allocation. To investigate the impact on the performance of the update interval duration, we have considered, in the single-bottleneck scenario, different values for Tu, viz. 40sand60s. Figures 7.5 and 7.6 show, respectively, the average total load accepted in the network and the corresponding total extra-revenue as a function of the total load offered to the network for Tu =40s. Furthermore,Figures7.7and7.8show the same performance metrics for Tu =60s. The average increase in the total accepted load, expressed as a percentage of the traffic admitted in the static allocation case, is of 14% for Tu = 40 s and 11% for Tu =60s, while for Tu = 20 s it was 24% (see Figure 7.3). These results allow to gauge the trade-off between performance improvement and overhead resulting from a more frequent execution of the allocation algorithms. We observe that an update interval value of 20 s leads to higher network revenue than other values. This is consistently observed across the whole range of offered loads below 90% of the maximum offered load. When the offered load increases beyond 90% of the maximum offered load, the system becomes overloaded and the impact of the update interval value becomes negligible. Note that the best performance is achieved with Tu =20 s; therefore, in the following, we choose an update interval of 20s in all network scenarios. In the same scenario of Figure 7.1 we then considered different utility functions for some sources. More specifically, the 8 connections having srk equal to 200 kb/s have associated the utility function equal to 0.5 · log(1 + x) while the 6 connections having srk equal to 500 kb/s have associated the utility function of 1.5 · log(1 + x). These utility functions are 60
Total Accepted Load (Mb/s) 6 5.5 5 4.5 4 3.5 3 2.5 Static Provisioning SDBA IDBA OBA 2 4 5 6 7 8 9 Average Offered Load (Mb/s) Figure 7.5: Average total accepted load as a function of the average load offered to the network of Figure 7.1 (Tu =40s) Network Extra−Revenue 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 Static Provisioning SDBA IDBA OBA 0 4 5 6 7 8 9 Average Offered Load (Mb/s) Figure 7.6: Average network extra-revenue as a function of the average load offered to the network of Figure 7.1 (Tu =40s) 61
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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
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 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
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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
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Page 63 and 64: Chapter 6 Mathematical Model This C
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Page 69 and 70: the two core nodes is equal to 6 Mb
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Page 75 and 76: plottedinFigure7.9. Figures 7.10 an
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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
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Page 95 and 96: Chapter 9 Conclusion 9.1 Discussion
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Page 100 and 101: [7] F. P. Kelly, “Charging and ra
Page 102 and 103: [26] P. Reichl, S. Leinen, and B. S
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
Page 117 and 118: List of Figures 1 Notre architectur
Page 119 and 120: 8.1 Performance upper bound for all
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