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Network tomography: identifiability and fourier domain estimation
 Proc. IEEE INFOCOM’07
, 2007
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Statistical Inverse Problems in Active Network Tomography
"... Abstract: Active network tomography includes several interesting statistical inverse problems that arise in the context of computer and communication networks. The primary goal in these problems is to recover linklevel information about qualityofservice parameters from aggregate endtoend data m ..."
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Abstract: Active network tomography includes several interesting statistical inverse problems that arise in the context of computer and communication networks. The primary goal in these problems is to recover linklevel information about qualityofservice parameters from aggregate endtoend data measured on paths across the network. The estimation and monitoring of these parameters are of considerable interest to network engineers and Internet service providers. This paper provides a review of the inverse problems and recent research on inference for loss rates and delay distributions. Some new results on parametric inference for delay distributions are developed. The results are illustrated using a network application related to Internet telephony. 1. The Inverse Problems Consider a tree T = {V, E} with a set of nodes V and a set of links or edges E. Figure 1 shows two examples: a simple twolayer symmetric binary tree on the left and a more general fourlayer tree on the right. Each member of E is a directed link numbered after the node at its terminus. V includes a root node 0, a set of receiver or destination nodes R and a set of internal nodes I. All transmissions on the tree are initiated at the root node. The internal nodes have a single incoming link and at least two outgoing links (children). The receiver nodes have a single incoming link but no
Loss tomography from tree topologies to general topologies. arXiv:1105.0054
, 2011
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Link Delay Estimation via Expander Graphs
, 2012
"... In network tomography, we seek to infer the status of parameters (such as delay) for links inside a network through endtoend probing between (external) boundary nodes along predetermined routes. In this work, we apply concepts from compressed sensing for network topologies that are expanders, to t ..."
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In network tomography, we seek to infer the status of parameters (such as delay) for links inside a network through endtoend probing between (external) boundary nodes along predetermined routes. In this work, we apply concepts from compressed sensing for network topologies that are expanders, to the delay estimation problem. We first show that a relative majority of network topologies are not expanders for the existing error bounds. Motivated by this, we relax this bound leading to evidence that for 30 % more networks, the link delays can be estimated. We provide simulation performance analysis of delay estimation based on l1 minimization, showing that accurate estimation is feasible for an increasing proportion of networks.
Link Delay Inference in ANA
"... Estimating quality of service (QoS) parameters such as link delay distribution from the endtoend delay of a multicast tree topology in network tomography cannot be achieved without multicast probing techniques or designing unicast probing packets that mimic the characteristics of multicast probing ..."
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Estimating quality of service (QoS) parameters such as link delay distribution from the endtoend delay of a multicast tree topology in network tomography cannot be achieved without multicast probing techniques or designing unicast probing packets that mimic the characteristics of multicast probing packets. Active probing is gradually giving way to passive measurement techniques. With the emergence of next generation networks such as Autonomic Network Architecture (ANA) network, which do not support active probing, a new way of thinking is required to provide network tomography support for such networks. This thesis is about investigating the possible solution to such problem in network tomography. Two approaches, queue model and adaptive learning model were implemented to minimized the uncertainty in the endtoend delay measurements from passive data source so that we could obtain endtoend delay measurements that exhibit the characteristics of unicast or multicast probing packets. The result shows that the adaptive learning model performs better than the queue model. In spite of its good performance against the queue model, it fails to outperform the unicast model. Overall, the correlation between the adaptive learning model and multicast probing model is quite weak when the traffic intensity is low and strong when the traffic intensity is high. The
DOI: 10.1214/074921707000000049 Statistical inverse problems in active network tomography
"... Abstract: The analysis of computer and communication networks gives rise to some interesting inverse problems. This paper is concerned with active network tomography where the goal is to recover information about qualityofservice (QoS) parameters at the link level from aggregate data measured on e ..."
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Abstract: The analysis of computer and communication networks gives rise to some interesting inverse problems. This paper is concerned with active network tomography where the goal is to recover information about qualityofservice (QoS) parameters at the link level from aggregate data measured on endtoend network paths. The estimation and monitoring of QoS parameters, such as loss rates and delays, are of considerable interest to network engineers and Internet service providers. The paper provides a review of the inverse problems and recent research on inference for loss rates and delay distributions. Some new results on parametric inference for delay distributions are also developed. In addition, a real application on Internet telephony is discussed. 1. The inverse problems Consider a topology with a tree structure defined as follows: T = {V, E} has a set of nodes V and a set of links or edges E. Figure 1 shows two examples, a simple twolayer symmetric binary tree on the left and a more general fourlayer tree on the right. Each member of E is a directed link numbered after the node at its terminus. V includes a (single) root node 0, a set of receiver or destination nodes R, and a set of internal nodes I. The internal nodes have a single incoming link and at least two outgoing links (children). The receiver nodes have a single incoming link but no children. For the tree on the right panel of Figure 1, R =