Topological sorting of large networks
Communications of the ACM
Maximum likelihood network topology identification from edge-based unicast measurements
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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)
Understanding internet topology: principles, models, and validation
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
Resolving IP aliases in building traceroute-based internet maps
IEEE/ACM Transactions on Networking (TON)
Efficient and dynamic routing topology inference from end-to-end measurements
IEEE/ACM Transactions on Networking (TON)
Toward the practical use of network tomography for internet topology discovery
INFOCOM'10 Proceedings of the 29th conference on Information communications
Efficent algorithm of energy minimization for heterogeneous wireless sensor network
EUC'06 Proceedings of the 2006 international conference on Embedded and Ubiquitous Computing
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
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Accurate and timely identification of the router-level 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. Unfortunately, limitations of prior tomographic techniques make timely resolution of large-scale topologies impossible due to the requirement of an infeasible number of measurements. In this paper, we describe new techniques that aim toward efficient tomographic inference for accurate router-level topology measurement. We introduce methodologies based on Depth-First Search (DFS) ordering that clusters end-hosts based on shared infrastructure and enables the logical tree topology of a network to be recovered accurately and efficiently. We evaluate the capabilities of our algorithms in large-scale simulation and find that our methods will reconstruct topologies using less than 2%of the measurements required by exhaustive methods and less than 15% of the measurements needed by the current state-of-the-art tomographic approach. We also present results from a study of the live Internet where we show our DFS-based methodologies can recover the logical router-level topology more accurately and with fewer probes than prior techniques.