Unicast Inference of Additive Metrics in General Network Topologies

Rahali, Mohamed; Sanner, Jean-Michel; Rubino, Gerardo

doi:10.1109/mascots.2019.00021

Cited by 7 publications

(9 citation statements)

References 15 publications

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“…To infer more general network topology, Rahali et al [30] proposed two statistical generic methods expectationmaximization and evolutionary sampling for inference of additive metrics using unicast probing. Rai et al [31] used path interference to identify general topology which outperformed the algorithm that used distance measurements.…”

Section: Related Workmentioning

confidence: 99%

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Network Topology Inference Based on Subset Structure Fusion

Fei

Zhai

et al. 2020

IEEE Access

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Network topology measurement is an important component in network research. Network tomography is able to accurately infer network topology by using end-to-end measurement without cooperation of internal routers. Unfortunately, traditional network tomography methods can not accurately estimate topology in the non-stationary network due to the variability of traffic distribution. In this paper, we present a novel network topology inference method based on subset structure fusion for accurate topology inference in the non-stationary network. First, we propose an end-to-end measurement method named threepacket to accurately probe the three-leaf-nodes subset structures of the network without the assumption that the packet delay or loss follows a stable distribution. Second, we propose a metric for the shared path length based on the structural characteristics of the subset structures to fuse these subset structures into a correct complete topology. The analytical and simulation results show that our method is more applicable for topology inference in the non-stationary network compared with the existing methods. INDEX TERMS End-to-end measurement,network tomography,non-stationary network,subset structure fusion,topology inference.

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Section: Related Workmentioning

confidence: 99%

“…The shared path length metric ρ can be used to recover the topology if it is an addictive metric [31]. So it needs to meet the following two conditions:…”

Section: Subset Structure Fusionmentioning

confidence: 99%

Network Topology Inference Based on Subset Structure Fusion

Fei

Zhai

et al. 2020

IEEE Access

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“…The "Anomaly Detection" module aggregates the end-to-end performances from the monitors to make a deeper analysis of the collected data. The FEAL framework integrates our previously proposed algorithm for the inference of additive metrics called ESA (Evolutionary Sampling Algorithm) [1]. The ESA algorithm computes the probability distribution for each link metric.…”

Section: E Anomaly Detection Modulementioning

confidence: 99%

“…Meanwhile, we compare two conditions to be satisfied: covering the network topology, as in most previous works, and adding more conditions on the probed cycles to enhance the anomaly localization, following an algebraic modeling of the problem. The "Anomaly Detection" module is based essentially on a statistical approach for additive metrics inference that we proposed in [1], here adapted to a more general case.…”

Section: Introductionmentioning

confidence: 99%

FEAL: A source routing Framework for Efficient Anomaly Localization

Rahali

Sanner

Rubino

2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Source routing represents a good opportunity to enhance monitoring solutions, particularly probing techniques. This technique allows deploying customized probing schemes to fulfill different monitoring needs like troubleshooting or Service Level Agreement (SLA) supervision. In this context, the use of probing cycles is a promising monitoring method. The deployment of such probing schemes becomes easier thanks to source routing since it allows constraining the traffic to follow specific paths.In this paper we propose the FEAL monitoring framework (Framework for Efficient Anomaly Localization) based on source routing probing cycles. The framework is mainly composed of two parts: the "Probing Cycles" and the "Anomaly Detection" modules. The first one defines the probing strategy by deploying the needed monitors and finding the probing cycles to cover the network topology. The "Anomaly Detection" module is based on our previously proposed statistical algorithm for the inference of link metrics named ESA (Evolutionary Sampling Algorithm) [1], here extended to more general classes of metrics. We prototype and evaluate the FEAL framework with a P4 implementation of source routing over a Mininet emulator. The results show that our framework detects and localizes efficiently the failure points in the 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%

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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Unicast Inference of Additive Metrics in General Network Topologies

Cited by 7 publications

References 15 publications

Network Topology Inference Based on Subset Structure Fusion

Network Topology Inference Based on Subset Structure Fusion

FEAL: A source routing Framework for Efficient Anomaly Localization

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

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