Network Topology Discovery through Self-Constrained Decisions

Network Topology Discovery is crucial to a number of network management tasks. Traditional topology discovery techniques require internal nodes to take actions on measurement packets, which makes them unpractical in many cases. For these reasons, tomographic techniques have been introduced, which allow for the reconstruction of network topologies with no need for cooperation from internal routers. The usual approach to tomographic topology discovery is based on clustering nodes into tree structures according to soft similarity metrics. We recently proposed a novel technique based on decision theoretic considerations that help the topology reconstruction by limiting the set of hypotheses to a finite and well-defined set, thus determining hard metrics. In the scheme, probe traffic is sent to all couples of end-nodes and a metric is assigned to each measurement. In this paper, we extend the technique by ordering the topology reconstruction procedure according to metrics reliability defined in terms of their variances. The algorithms presented in the paper are validated through extensive simulations in several network scenarios. The results show that such a methodology allows to retrieve a complete picture of the network that includes the detection of all the internal nodes along with the values of capacities of the interconnecting links.