Robust network tomography: K-Identifiability and monitor assignment

Ren, Wei; Dong, Wei

doi:10.1109/infocom.2016.7524375

Cited by 20 publications

(14 citation statements)

References 18 publications

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“…In [14] it was recently introduced XPath a practical way to implement explicit path control. Details can be found on both [18] and [14]. In our case we briefly mention that XPath can easily implement the routing CAP − (as well as CSP).…”

Section: Discussionmentioning

confidence: 99%

Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography

Galesi

Ranjbar

2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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We study maximal identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. We prove tight upper and lower bounds on the maximal identifiability of failure nodes for specific classes of network topologies, such as trees and d-dimensional grids, in both directed and undirected cases. We prove that directed d-dimensional grids with support n have maximal identifiability d using 2d(n − 1) + 2 monitors; and in the undirected case we show that 2d monitors suffice to get identifiability of d − 1. We then study identifiability under embeddings: we establish relations between maximal identifiability, embeddability and graph dimension when network topologies are modeled as DAGs. Our results suggest the design of networks over N nodes with maximal identifiability Ω(log N ) using O(log N ) monitors and a heuristic to boost maximal identifiability on a given network by simulating d-dimensional grids. We provide positive evidence of this heuristic through data extracted by exact computation of maximal identifiability on examples of small real networks. * A preliminary version of this paper appeared in [11].

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Section: Discussionmentioning

confidence: 99%

Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography

Galesi

Ranjbar

2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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“…Ma et al [5] proposed a novel monitor placement algorithm for the failed node localisation problem. This placement algorithm approach has featured heavily in the literature in recent years, with further works extending the original algorithm to account for changes to network tomography [6], proposing an algorithm designed for inferring city road traffic [7], and relaxing assumptions about network reliability and taking a topological approach to algorithm design [8].…”

Section: Monitor Placement and The Failed Node Localisation Problemmentioning

confidence: 99%

Node Failure Localisation Problem for Load Balancing Dynamic Networks

Barnes¹,

Hole²

2021

Preprint

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Network tomography has been used as an approach to the Node Failure Localisation problem, whereby misbehaving subsets of nodes in a network are to be determined. Typically approaches in the literature assume a statically routed network, permitting linear algebraic arguments. In this work, a load balancing, dynamically routed network is studied, necessitating a stochastic representation of network dynamics. A network model was developed, permitting a novel application of Markov Chain Monte Carlo (MCMC) inference to the Node Failure Localisation (NFL) problem, and the assessment of monitor placement choices. Two nuanced monitor placement algorithms, including one designed for the NFL problem by Ma et al. 2014 were tested, with the published algorithm performing significantly better.

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“…Other notable contribution in the field of traffic matrix estimation include, Bayesian Learning techniques for traffic matrix estimation, investigated by Nie [23] and Xiaobo [24]. Other researchers [25][26][27][28] have focused their attention on the optimal placement of network monitors for scalable monitoring of the network. Boolean Network Tomography has been a recent topic of interest [29][30].…”

Section: Related Workmentioning

confidence: 99%

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

Qazi¹,

Atif²,

Kadri³

2018

KSII TIIS

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Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under-constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

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Robust network tomography: K-Identifiability and monitor assignment

Cited by 20 publications

References 18 publications

Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography

Tight Bounds for Maximal Identifiability of Failure Nodes in Boolean Network Tomography

Node Failure Localisation Problem for Load Balancing Dynamic Networks

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

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