Unicast Inference of Additive Metrics in General Network Topologies

Internet tomography studies the inference of the internal network performances from end-to-end measurements. Unicast probing can be advantageous for such monitoring solutions due to the wide support of unicast and the easy deployment of unicast probing paths. In this work, we propose two statistical generic methods for the inference of additive metrics using unicast probing. Our solutions give more flexibility in the choice of the collection points placement, the probed paths and they are not limited to specific topologies. Firstly, we propose the k-paths method that extends the applicability of a previously proposed solution called Flexicast for tree topologies. It is based on the Expectation-Maximization (EM) algorithm which is characterized by high computational and memory complexities. Secondly, we propose the Evolutionary Sampling Algorithm (ESA) that enhances the accuracy and the computing time but following a different approach.