Algorithms for Network Topology Discovery using End-to-End Measurements

Identifying and inferring performances of a network topology is a well known problem. Achieving this by using only end-to-end measurements at the application level is a method known as network tomography. When the topology produced reflects capacities of sets of links with respect to a metric, the topology is called a Metric-Induced Network Topology (MINT). Tomography producing MINT has been widely used in order to predict performances of communications between clients and server. Nowadays grids connect up to thousands communicating resources that may interact in a partially or totally coordinated way. Consequently, applications running upon this kind of platform often involve massively concurrent bulk data transfers. This implies that the client/server model is no longer valid. In this paper, we introduce new algorithms that reconstruct a novel representation of the knowledge inferred from the network which is able to deal with multiple sources/multiple destinations transfers.