Section: Scientific Foundations

Stochastic modelling

The scientific foundations of our modelling activities are composed of stochastic processes theory and, in particular, Markov processes, queuing theory, graph theory, etc., either for analytical models or for discrete event simulation or Monte Carlo (and Quasi-Monte Carlo) techniques. We are always interested in models' evaluation techniques for dependability and performability analysis, both in static (network reliability) and dynamic contexts (depending on the fact that time plays an explicit role in the analysis or not). We look at models from the classical so-called call level , leading to standard models (for instance, queuing models) and also at the burst level , leading to fluid models . For this more recent research field, we work both on analytical techniques and on discrete event simulation.

Lastly, our work on the design of the topologies of WANs leads us to optimization techniques, in particular in the case of very large optimization problems, usually formulated in terms of graphs. The associated methods we are interested in are composed of simulated annealing, genetic algorithms, TABU search, etc. For the time being, we have obtained our best results with GRASP techniques.