Topological sorting of large networks
Communications of the ACM
Measuring ISP topologies with rocketfuel
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
Network radar: tomography from round trip time measurements
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Network tomography from measured end-to-end delay covariance
IEEE/ACM Transactions on Networking (TON)
Touring the internet in a TCP sidecar
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Network loss tomography using striped unicast probes
IEEE/ACM Transactions on Networking (TON)
Orbis: rescaling degree correlations to generate annotated internet topologies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Discarte: a disjunctive internet cartographer
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Efficent algorithm of energy minimization for heterogeneous wireless sensor network
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
Hierarchical Inference of Unicast Network Topologies Based on End-to-End Measurements
IEEE Transactions on Signal Processing
Likelihood based hierarchical clustering
IEEE Transactions on Signal Processing
Deployment of an Algorithm for Large-Scale Topology Discovery
IEEE Journal on Selected Areas in Communications
Efficient network tomography for internet topology discovery
IEEE/ACM Transactions on Networking (TON)
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Accurate and timely identification of the routerlevel topology of the Internet is one of the major unresolved problems in Internet research. Topology recovery via tomographic inference is potentially an attractive complement to standard methods that use TTL-limited probes. In this paper, we describe new techniques that aim toward the practical use of tomographic inference for accurate router-level topology measurement. Specifically, prior tomographic techniques have required an infeasible number of probes for accurate, large scale topology recovery. We introduce a Depth-First Search (DFS) Ordering algorithm that clusters end host probe targets based on shared infrastructure, and enables the logical tree topology of the network to be recovered accurately and efficiently. We evaluate the capabilities of our DFS Ordering topology recovery algorithm in simulation and find that our method uses 94% fewer probes than exhaustive methods and 50% fewer than the current state-of-the-art. We also present results from a case study in the live Internet where we show that DFS Ordering can recover the logical router-level topology more accurately and with fewer probes than prior techniques.