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

in the literature to predict the near-term transmission rate of a connection based on
past traffic measurements; however, the focus of this thesis is not to find out the best
traffic predictor.
In this work we only rely on the last measured value, b n−1
k , and we propose the fol-
lowing simple bandwidth allocation: rn k =min{2·bn−1 k ,srk}, ∀k ∈ Kng. In this regard
we are currently studying more efficient traffic predictors that can allow improved
bandwidth allocation. We study the impact of this choice in Chapter 8, through the
performance of the mathematical model.
• Greedy connections are assigned in this step their subscribed rate srk, and they
Step 2
further take part to the allocation of extra-bandwidth performed in Step 2, since
they are already exploiting all their subscribed rate.
After having performed the allocations described in Step 1, each algorithm individuates on
each link l the residual bandwidth Rl, i.e. the spare bandwidth as well as the bandwidth left
unused by idle and non-greedy connections. Rl is hence given by the following expression:
Rl = Cl − (
k∈Ki∪Kng
r n k · a l k +
srk · a l k ), ∀l ∈ L (5.1)
where the first summation represents the total bandwidth allocated in Step 1 to idle and
non-greedy connections, while the second summation represents the bandwidth allocated
to greedy connections. Such extra-bandwidth is distributed exclusively to greedy users
k∈Kg
who can exploit it to transmit at a rate higher than their subscribed rate.
To this aim, we propose in the following three heuristics for the bandwidth alloca-
tion problem and in the next Chapter, we further illustrate a mathematical model which
provides an upper bound to the network revenue that can be obtained by any dynamic
bandwidth allocation scheme.
43