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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. In order to optimize overall completion times, those transfers have to be scheduled based on knowledge about network performances and topology. Identifying and inferring performances of a network topology is a classic problem. Achieving this by using only end-to-end measurements at the application level is a method known as network tomography. When topology reflects capacities of sets of links with respect to a metric, the model used to represent the topology obtained is called a Metric-Induced Network Topology (MINT). Such a type of representation, obtained using statistical methods, has been widely used in order to represent performances of client/server communication protocols. However, it is no longer accurate when dealing with grids. In this paper, we present a novel representation of the infered knowledge from multiple source and multiple destination measurements.

Metric Induced Network Poset (MINP): A model of the network from an application point of view