Network Tomography: A Review and Recent Developments

Lawrence, Earl; Michailidis, George; Nair, Vijayan N.; Xi, Bowei

doi:10.1142/9781860948886_0016

Cited by 78 publications

(57 citation statements)

References 26 publications

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“…Sub-trees and unicast are employed in [5,14] as alternatives, due to the inflexibility of multicasting to all receivers. Employing multicast, [5,15] derive the necessary and sufficient conditions on the multicast tree for identifying all link metric distributions. If most links do not exhibit severe losses or delays, [10] proposes algorithms to identify the worst performing links.…”

Section: Further Discussion On Related Workmentioning

confidence: 99%

“…At the end of Section III-B in [19], the authors raise the question whether or not monitoring non-simple paths (i.e., paths that may contain cycles) between two monitors suffices to identify all link metrics in the network 5 . According to Corollary 4.1, the exterior links cannot be identified even if all the interior link metrics are known; allowing cycles in the interior graph H provides no additional information regarding the exterior links.…”

Section: Discussion On Paths With Cyclesmentioning

confidence: 99%

“…Network tomography [4] refers to the methodology of inferring internal network characteristics through end-to-end measurements. Without requiring special cooperation from internal nodes, network tomography can utilize measurements from data packets to obtain path-level information [5], thus reducing the need for active probes.…”

Section: Introductionmentioning

confidence: 99%

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Identifiability of link metrics based on end-to-end path measurements

He²,

Leung

et al. 2013

Proceedings of the 2013 Conference on Internet Measurement Conference

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We investigate the problem of identifying individual link metrics in a communication network from end-to-end path measurements, under the assumption that link metrics are additive and constant. To uniquely identify the link metrics, the number of linearly independent measurement paths must equal the number of links. Our contribution is to characterize this condition in terms of the network topology and the number/placement of monitors, under the constraint that measurement paths must be cycle-free. Our main results are: (i) it is generally impossible to identify all the link metrics by using two monitors; (ii) nevertheless, metrics of all the interior links not incident to any monitor are identifiable by two monitors if the topology satisfies a set of necessary and sufficient connectivity conditions; (iii) these conditions naturally extend to a necessary and sufficient condition for identifying all the link metrics using three or more monitors. We show that these conditions not only allow efficient identifiability tests, but also enable an efficient algorithm to place the minimum number of monitors in order to identify all link metrics. Our evaluations on both random and real topologies show that the proposed algorithm achieves identifiability using a much smaller number of monitors than a baseline solution.

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Section: Further Discussion On Related Workmentioning

confidence: 99%

Section: Discussion On Paths With Cyclesmentioning

confidence: 99%

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Identifiability of link metrics based on end-to-end path measurements

He²,

Leung

et al. 2013

Proceedings of the 2013 Conference on Internet Measurement Conference

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“…In words, MMP works by ensuring that: (i) all nodes with degree (i.e., number of neighbors) 1 or 2 are monitors, (ii) all bi/tri-connected components with at least three nodes have at least three nodes that are separation vertices or monitors, and (iii) all connected components with at least three nodes have at least three monitors; see [10] for details 5 . In static networks, such a monitor placement is guaranteed to be sufficient and optimal in the following sense.…”

Section: B Minimum Monitor Placement For Static Networkmentioning

confidence: 99%

“…Traditionally, network monitoring systems rely on diagnostic tools such as traceroute, pathchar [3], and Network Characterization Service (NCS) [4] to directly measure the performance of individual links via active probes, but these techniques suffer from high measurement overhead and lack of cooperation from internal nodes. In contrast, tomography-based monitoring only requires cooperation of monitors and can utilize passive measurements from data packets to reduce the need of active probes [5].…”

Section: Introductionmentioning

confidence: 99%

Robust and Efficient Monitor Placement for Network Tomography in Dynamic Networks

Gkelias

Ma³

et al. 2017

IEEE/ACM Trans. Networking

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Abstract-We consider the problem of placing the minimum number of monitors in a dynamic network to identify additive link metrics from path metrics measured along cycle-free paths between monitors. Our goal is robust monitor placement, i.e., the same set of monitors can maintain network identifiability under topology changes. Our main contribution is a set of monitor placement algorithms with different performance-complexity tradeoffs that can simultaneously identify multiple topologies occurring during the network lifetime. In particular, we show that the optimal monitor placement is the solution to a generalized hitting set problem, for which we provide a polynomial-time algorithm to construct the input and a greedy algorithm to select the monitors with logarithmic approximation. Although the optimal placement is NP-hard in general, we identify non-trivial special cases that can be solved efficiently. Our secondary contribution is a dynamic triconnected decomposition algorithm to compute the input needed by the monitor placement algorithms, which is the first such algorithm that can handle edge deletions. Our evaluations on mobility-induced dynamic topologies verify the efficiency and the robustness of the proposed algorithms.

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Evaluation of mathematical models to estimate end‐to‐end traffic in a backbone network

Emami

Akbari

Zamani

2018

Int J Communication

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End-to-end network traffic analysis is one of the most important factors in design, development, and traffic engineering of large-scale backbone networks. Despite the importance of end-to-end traffic, it is not applicable to measure it directly using protocols such as NetFlow in large-scale networks due to high computational cost. One technique to tackle this problem is estimating endto-end traffic using easily measured neighboring link loads. Tomography model relates these two parameters with linear equations. In this way, the problem turns into an inverse problem. Since the number of link loads is usually smaller than the number of end-to-end flows in a computer network, traffic matrix estimation is considered to be an ill-posed problem. In this paper, we propose two different methods for estimation of end-to-end traffic matrix by means of link load measurements. One of the methods deploys Kalman filter and the other is developed based on Tikhonov regularization with respect to the existence of temporal autocorrelation among traffics. We also propose a best estimation model based on least squares, which explores the temporal autocorrelation in end-to-end traffics. This model is used as the best estimator and has been compared with other methods. We have evaluated the efficiency of the proposed methods using real datasets from Abilene and GÉANT backbone networks. It is worth noting that the proposed methods in this paper also can be used in any inverse problem with temporal autocorrelation

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Network Tomography: A Review and Recent Developments

Cited by 78 publications

References 26 publications

Identifiability of link metrics based on end-to-end path measurements

Identifiability of link metrics based on end-to-end path measurements

Robust and Efficient Monitor Placement for Network Tomography in Dynamic Networks

Evaluation of mathematical models to estimate end‐to‐end traffic in a backbone network

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