Section: New Results

Large Scale Networks Performance and Modeling

We propose in [14] , a new model for peer-to-peer networking which takes the network bottlenecks into account beyond the access. This model can cope with key features of P2P networking like degree or locality constraints together with the fact that distant peers often have a smaller rate than nearby peers. Using a network model based on rate functions, we give a closed form expression of peers download performance in the system's fluid limit, as well as approximations for the other cases. Our results show the existence of realistic settings for which the average download time is a decreasing function of the load, a phenomenon that we call super-scalability.

Actuellement, une large part du trafic Internet vient de sites de "User-Generated Content" (UGC). Comprendre les caractéristiques de ce trafic est important pour les opérateurs (dimensionnement réseau), les fournisseurs (garantie de la qualité de service) et les équipementiers (conception d'équipements adaptés). Dans ce contexte, nous proposons [15] , d'analyser et de modéliser des traces d'usage du site DailyMotion.

In [13] , we aim at analyzing the classical information spreading "push" protocol in dynamic networks. We consider the edge-Markovian evolving graph model which captures natural temporal dependencies between the structure of the network at time $t$, and the one at time $t+1$. Precisely, a non-edge appears with probability $p$, while an existing edge dies with probability $q$. In order to fit with real-world traces, we mostly concentrate our study on the case where $p=\Omega \left(\frac{1}{n}\right)$ and $q$ is constant. We prove that, in this realistic scenario, the "push" protocol does perform well, completing information spreading in $O(logn)$ time steps, w.h.p., even when the network is, w.h.p., disconnected at every time step (e.g., when $p\ll \frac{logn}{n}$). The bound is tight. We also address other ranges of parameters $p$ and $q$ (e.g., $p+q=1$ with arbitrary $p$ and $q$, and $p=\Theta \left(\frac{1}{n}\right)$ with arbitrary $q$). Although they do not precisely fit with the measures performed on real-world traces, they can be of independent interest for other settings. The results in these cases confirm the positive impact of dynamism.