Active Learning of Multiple Source Multiple Destination Topologies

@article{Sattari2013ActiveLO,
  title={Active Learning of Multiple Source Multiple Destination Topologies},
  author={Pegah Sattari and Maciej Kurant and Anima Anandkumar and Athina Markopoulou and Michael G. Rabbat},
  journal={IEEE Transactions on Signal Processing},
  year={2013},
  volume={62},
  pages={1926-1937}
}
We consider the problem of inferring the topology of a network with M sources and N receivers (an M-by- N network), by sending probes between the sources and receivers. Prior work has shown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (1-by- N's or 2-by-2's) and then merge them to identify the M-by- N topology. We focus on the second part, which had previously received less attention in the literature. We assume that a 1-by- N topology is given… 
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References

SHOWING 1-10 OF 46 REFERENCES
Topology inference using network coding
Our goal, in this paper, is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple
Multiple source multiple destination topology inference using network coding
TLDR
The first contribution is to show that, when intermediate nodes perform network coding, topological information contained in network coded packets allows to accurately distinguish among all different 2-by-2 subnetwork components, which was not possible with traditional tomographic techniques.
Active topology inference using network coding
Multiple-Source Internet Tomography
TLDR
This work demonstrates that the general multiple source, multiple destination tomography problem can be formally reduced to the two source, two destination case, allowing the immediate generalization of any sampling techniques developed for the simpler, smaller scenario.
Inference of multicast routing trees and bottleneck bandwidths using end-to-end measurements
  • S. Ratnasamy, S. McCanne
  • Computer Science
    IEEE INFOCOM '99. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No.99CH36320)
  • 1999
TLDR
This paper explores the problem of inferring the internal structure of a multicast distribution tree using only observations made at the end hosts and shows that the algorithm is robust and appears to converge to the correct tree with high probability.
Topology discovery of sparse random graphs with few participants
TLDR
It is demonstrated that while consistent discovery is tractable for sparse random graphs using a small number of participants, in general, there are graphs which cannot be discovered by any algorithm even with a significant number of Participants, and with the availability of end-to-end information along all the paths between the participants.
Merging logical topologies using end-to-end measurements
TLDR
This work develops a multiple source active probing methodology and statistical framework for testing whether the paths from two sources to two receivers branch at a common internal node, to address the problem of merging multiple tree topologies.
Robust multi-source network tomography using selective probes
TLDR
The first algorithm accommodates measurements perturbed by additive noise, while the second considers a novel noise model that captures missing measurements and the network's deviations from a tree topology.
Hierarchical Inference of Unicast Network Topologies Based on End-to-End Measurements
TLDR
This paper provides a framework that directly deals with general logical tree topologies in unicast logical tree networks using end-to-end measurements and shows that the algorithm is more robust than binary-tree based methods.
Efficient and Dynamic Routing Topology Inference From End-to-End Measurements
TLDR
This paper proposes a general framework for designing topology inference algorithms based on additive metrics that can flexibly fuse information from multiple measurements to achieve better estimation accuracy and develops computationally efficient (polynomial-time) topology inferred algorithms.
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