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

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Chapter 6<br />

Mathematical Mo<strong>de</strong>l<br />

This Chapter introduces a novel and flexible mathematical mo<strong>de</strong>l, I<strong>de</strong>al Bandwidth Al-<br />

location (IBA), that maximizes the total n<strong>et</strong>work revenue and provi<strong>de</strong>s bounds to the<br />

performance achievable by any online dynamic bandwidth allocation algorithm.<br />

Such mo<strong>de</strong>l assumes the exact knowledge of the future traffic offered to the n<strong>et</strong>work.<br />

The bandwidth allocations are performed optimizing the operation of the n<strong>et</strong>work loa<strong>de</strong>d<br />

with the actual present and future traffic. No practical bandwidth allocation scheme can<br />

perform b<strong>et</strong>ter.<br />

This mathematical mo<strong>de</strong>l extends the optimization problem related to utility maximiza-<br />

tion introduced in [7] and its solution can be obtained using Lagrangian m<strong>et</strong>hods. Further,<br />

if the objective function can be approximated using piece-wise linear concave functions,<br />

the problem can be solved using standard linear programming (LP) techniques.<br />

IBA allows us to gauge the impact of the traffic predictor used to assign bandwidth to<br />

non-greedy connections (Section 5.1), as well as of the granularity in bandwidth assignment<br />

(i.e. the sensitivity to the param<strong>et</strong>er Tu) on the performance of our proposed dynamic<br />

bandwidth allocation algorithms.<br />

The structure of this Chapter is as follows. In Section 6.1, we first illustrate the n<strong>et</strong>work<br />

mo<strong>de</strong>l, then we <strong>de</strong>fine some notations nee<strong>de</strong>d for our mathematical mo<strong>de</strong>l. In Section 6.2,<br />

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