  Network tomography, a classic research problem in the realm of network
monitoring, refers to the methodology of inferring unmeasured network
attributes using selected end-to-end path measurements. In the research
community, network tomography is generally investigated under the assumptions
of known network topology, correlated path measurements, bounded number of
faulty nodes/links, or even special network protocol support. The applicability
of network tomography is considerably constrained by these strong assumptions,
which therefore frequently position it in the theoretical world. In this
regard, we revisit network tomography from the practical perspective by
establishing a generic framework that does not rely on any of these assumptions
or the types of performance metrics. Given only the end-to-end path performance
metrics of sampled node pairs, the proposed framework, NeuTomography, utilizes
deep neural network and data augmentation to predict the unmeasured performance
metrics via learning non-linear relationships between node pairs and underlying
unknown topological/routing properties. In addition, NeuTomography can be
employed to reconstruct the original network topology, which is critical to
most network planning tasks. Extensive experiments using real network data show
that comparing to baseline solutions, NeuTomography can predict network
characteristics and reconstruct network topologies with significantly higher
accuracy and robustness using only limited measurement data.
