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Multiple source multiple destination topology inference using network coding
 in Proc . of IEEE Symposium of Network Coding (NetCod
, 2009
"... Abstract — In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. W ..."
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Cited by 8 (2 self)
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Abstract — In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. We combine and extend two ideas that have been developed independently. On one hand, network coding introduces topologydependent correlation, which can then be exploited at the receivers to infer the topology [1]. On the other hand, it has been shown that a traditional (i.e., without network coding) multiple source, multiple receiver tomography problem can be decomposed into multiple two source, two receiver subproblems [2]. Our 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 2by2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internetlike topology, and is robust to the presence of delay variability and packet loss. I.
2009 Workshop on Network Coding, Theory and Applications Multiple Source Multiple Destination Topology Inference using Network Coding
"... Abstract In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. ..."
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Abstract In this paper, we combine network coding and tomographic techniques for topology inference. Our goal is to infer the topology of a network by sending probes between a given set of multiple sources and multiple receivers and by having intermediate nodes perform network coding operations. We combine and extend two ideas that have been developed independently. On one hand, network coding introduces topologydependent correlation, which can then be exploited at the receivers to infer the topology [1]. On the other hand, it has been shown that a traditional (i.e., without network coding) multiple source, multiple receiver tomography problem can be decomposed into multiple two source, two receiver subproblems [2]. Our 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 2by2 subnetwork components, which was not possible with traditional tomographic techniques. Our second contribution is to use this knowledge to merge the subnetworks and accurately reconstruct the general topology. Our approach is applicable to any general Internetlike topology, and is robust to the presence of delay variability and packet loss. I.
INVERSE PROBLEMS FOR RANDOM WALKS ON TREES: NETWORK TOMOGRAPHY
, 2006
"... Abstract. Let G be a finite tree with root r and associate to the internal vertices of G a collection of transition probabilities for a simple nondegenerate Markov chain. Embedd G into a graph G ′ constructed by gluing finite linear chains of length at least 2 to the terminal vertices of G. Then G ′ ..."
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Abstract. Let G be a finite tree with root r and associate to the internal vertices of G a collection of transition probabilities for a simple nondegenerate Markov chain. Embedd G into a graph G ′ constructed by gluing finite linear chains of length at least 2 to the terminal vertices of G. Then G ′ admits distinguished boundary layers and the transition probabilities associated to the internal vertices of G can be augmented to define a simple nondegenerate Markov chain X on the vertices of G ′. We show that the transition probabilities of X can be recovered from the joint distribution of first hitting time and first hitting place of X started at the root r for the distinguished boundary layers of G ′. 1.
LETTER Multipath Probing and Grouping in Multihomed Networks
"... SUMMARY We propose a novel probing scheme capable of discovering shared bottlenecks among multiple paths between two multihomed hosts simultaneously, without any specific help from the network routers, and a subsequent grouping approach for partitioning these paths into groups. Simulation results sh ..."
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SUMMARY We propose a novel probing scheme capable of discovering shared bottlenecks among multiple paths between two multihomed hosts simultaneously, without any specific help from the network routers, and a subsequent grouping approach for partitioning these paths into groups. Simulation results show that the probing and grouping have an excellent performance under different network conditions. key words: Multihomed, network probing, path diversity, shared bottleneck