Taming both predictable and unpredictable link failures for network tomography

Li, Huikang; Gao, Yi; Dong, Wei; Chen, Chun

doi:10.1145/3063955.3063990

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…The work of Ren et al [7] proposed algorithms to determine which link metrics can be identified and where to place monitors to maximize the number of identifiable links, subject to a bounded number of link failures. Additive metric tomography was also studied in [8], [9], to identify of additive link metrics under topology changes.…”

Section: Related Workmentioning

confidence: 99%

Static and Dynamic Failure Localization through Progressive Network Tomography

Arrigoni¹,

Bartolini²,

Massini³

et al. 2021

Preprint

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We aim at assessing the states of the nodes in a network by means of end-to-end monitoring paths. The contribution of this paper is twofold. First, we consider a static failure scenario. In this context, we aim at minimizing the number of probes to obtain failure identification. To face this problem we propose a progressive approach to failure localization based on stochastic optimization, whose solution is the optimal sequence of monitoring paths to probe. We address the complexity of the problem by proposing a greedy strategy in two variants: one considers exact calculation of posterior probabilities of node failures, given the observation, whereas the other approximates these values by means of a novel failure centrality metric. Secondly, we adapt these two strategies to a dynamic failure scenario where nodes states can change throughout a monitoring period. By means of numerical experiments conducted on real network topologies, we demonstrate the practical applicability of our approach. Our performance evaluation evidences the superiority of our algorithms with respect to state of the art solutions based on classic Boolean Network Tomography as well as approaches based on sequential group testing.

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Section: Related Workmentioning

confidence: 99%

Static and Dynamic Failure Localization through Progressive Network Tomography

Arrigoni¹,

Bartolini²,

Massini³

et al. 2021

Preprint

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“…Fan and Li [16] proposed a method of estimating link delay by using sparse Bayesian learning. Li et al [17] proposed a method of determining the minimum monitor arrangement under which the performance metric of all links can be estimated even when the topology is changed, based on the assumption that the monitor node can freely select the measurement paths. More recently, Tootaghaj et al [18] showed the optimal selection of monitoring paths considering the trade-off of identifiability and probing cost.…”

Section: Related Workmentioning

confidence: 99%

Network Tomography Using Routing Probability for Undeterministic Routing

Tagyo

Ikegami

Kawahara

2021

IEICE Trans. Commun.

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The increased performance of mobile terminals has made it feasible to collect data using users' terminals. By making the best use of the network performance data widely collected in this way, network operators should deeply understand the current network conditions, identify the performance-degraded components in the network, and estimate the degree of their performance degradation. For their demands, one powerful solution with such end-to-end data measured by users' terminals is network tomography. Meanwhile, with the advance of network virtualization by software-defined networking, routing is dynamically changed due to congestion or other factors, and each end-to-end measurement flow collected from users may pass through different paths between even the same origin-destination node pair. Therefore, it is difficult and costly to identify through which path each measurement flow has passed, so it is also difficult to naively apply conventional network tomography to such networks where the measurement paths cannot be uniquely determined. We propose a novel network tomography for the networks with undeterministic routing where the measurement flows pass through multiple paths in spite of the origin-destination node pair being the same. The basic idea of our method is to introduce routing probability in accordance with the aggregated information of measurement flows. We present two algorithms and evaluate their performances by comparing them with algorithms of conventional tomography using determined routing information. Moreover, we verify that the proposed algorithms are applicable to a more practical network.

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“…In this paper, we aim at the problem of how to understand the underlying routing topology from a node to a set of other nodes in a network, where all internal nodes (e.g., routers and three-layer switcher) may refuse to return any information about topology. The information of underlying routing topology of a network is particularly useful for many applications such as failure link diagnosis [25], [26], P2P network optimization [27], and link performance parameter inference [28]. For example, the works presented in [25] and [26] focus on the problem of discovering and localizing the failure links in networks by combining the topology and other path-level performance information under the assumption that the underlying routing topology is known.…”

Section: Problem Statement a Model And Assumptionmentioning

confidence: 99%

“…The information of underlying routing topology of a network is particularly useful for many applications such as failure link diagnosis [25], [26], P2P network optimization [27], and link performance parameter inference [28]. For example, the works presented in [25] and [26] focus on the problem of discovering and localizing the failure links in networks by combining the topology and other path-level performance information under the assumption that the underlying routing topology is known. Hence, understanding the underlying routing topology is a necessary preliminary work if those methods can be applied to actual networks.…”

Section: Problem Statement a Model And Assumptionmentioning

confidence: 99%

Network Topology Inference Using Higher-Order Statistical Characteristics of End-to-End Measured Delays

Fei

Wen

et al. 2020

IEEE Access

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Network topology is important information for many network control and management applications. Network tomography infers network topology from end-to-end measured packet delays or losses, which is more feasible than internal cooperation-based methods and attracts many studies. Most of the existing methods for network topology inference usually function under the assumption that the distribution of packet delay or loss follows a given distribution (e.g., Gaussian or Gaussian mixture), and they estimate network topology from the parameters of the given distribution. However, these methods may fail to obtain an accurate estimation because the real distribution of packet delay or loss usually cannot be described by a certain distribution. In this paper, we present a novel network topology inference method based on the unicast end-to-end measured delays. The method abandons the assumption of packet delay distribution and constructs network topology by inferring the higher-order cumulants of internal links from the end-to-end measured delays. The analytical and simulation results show that the proposed method offers over 10% improvement in accuracy compared with that of the state-of-the-art works. INDEX TERMS Higher-order cumulant, topology inference, end-to-end measurement, network tomography.

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Taming both predictable and unpredictable link failures for network tomography

Cited by 7 publications

References 27 publications

Static and Dynamic Failure Localization through Progressive Network Tomography

Static and Dynamic Failure Localization through Progressive Network Tomography

Network Tomography Using Routing Probability for Undeterministic Routing

Network Topology Inference Using Higher-Order Statistical Characteristics of End-to-End Measured Delays

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