Soft Failure Localization Using Machine Learning with SDN-based Network-wide Telemetry

Mayer, Kayol Soares; Soares, Jonathan Aguiar; Pinto, Rossano P.; Rothenberg, Christian Esteve; Arantes, Dalton Soares; Mello, Darli A. A.

doi:10.1109/ecoc48923.2020.9333313

Cited by 17 publications

(9 citation statements)

References 22 publications

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“…The importance of knowledge about network topology for efficient failure detection and localization is emphasized in [16]. Failure detection and localization in optical networks is hot-topic area because these networks usually represent backbone and core of service provider communication network infrastructure [17][18][19][20][21][22]. Two types of failures can be observed, hard and soft failures.…”

Section: Related Workmentioning

confidence: 99%

“…Typically, machine learning and neural networks are used for soft failure detection in optical networks [17][18][19]. Similarly, machine learning can be used for failure localization in optical networks as well [20][21][22]. The proposed machine learning-based solutions require some data (such as power spectrum density [18], bit error rate samples [19], routed lightpaths [20], mean time between failures [21], etc.)…”

Section: Related Workmentioning

confidence: 99%

“…Similarly, machine learning can be used for failure localization in optical networks as well [20][21][22]. The proposed machine learning-based solutions require some data (such as power spectrum density [18], bit error rate samples [19], routed lightpaths [20], mean time between failures [21], etc.) from the optical network to perform failure detection or localization.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Simakovic

Cica

2021

Electronics

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Modern HFC (Hybrid Fiber–Coaxial) networks comprise millions of users. It is of great importance for HFC network operators to provide high network access availability to their users. This requirement is becoming even more important given the increasing trend of remote working. Therefore, network failures need to be detected and localized as soon as possible. This is not an easy task given that there is a large number of devices in typical HFC networks. However, the large number of devices also enable HFC network operators to collect enormous amounts of data that can be used for various purposes. Thus, there is also a trend of introducing big data technologies in HFC networks to be able to efficiently cope with the huge amounts of data. In this paper, we propose a novel mechanism for efficient failure detection and localization in HFC networks using a big data platform. The proposed mechanism utilizes the already present big data platform and collected data to add one more feature to big data platform—efficient failure detection and localization. The proposed mechanism has been successfully deployed in a real HFC network that serves more than one million users.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Simakovic

Cica

2021

Electronics

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“…New monitoring technologies have emerged to provide accurate network awareness to the control and orchestration system. A first example is telemetry [3][4][5], enabling network nodes to stream performance parameters such as bit error rate (BER) and optical power levels even every few seconds, at a pace significantly higher than traditional solutions e.g. based on Simple Network Management Protocol (SNMP).…”

Section: Introductionmentioning

confidence: 99%

Extending P4 in-band telemetry to user equipment for latency- and localization-aware autonomous networking with AI forecasting

Scano

Paolucci

Kondepu

et al. 2021

J. Opt. Commun. Netw.

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In Beyond-5G Networks, detailed end-to-end monitoring of specific application traffic will be required along with the access-backhaul-cloud continuum to enable low latency service thanks to local edge steering. Current monitoring solutions are confined to specific network segments. In-band telemetry (INT) technologies for SDN programmable data planes based on the P4 language are effective in the backhaul network segment, although limited to inter-switch latency, therefore link latencies including wireless and optical segments are excluded from INT monitoring. Moreover, information such as user equipment geolocation information would allow detailed mobility monitoring and improved cloud-edge steering policies. However, the synchronization between latency and location information, typically provided by different platforms, is hard to achieve with current monitoring systems. In this paper P4-based INT is proposed to be thoroughly extended involving the user equipment (UE). The novel INT mechanism is designed to provide synchronized and accurate end-to-end latency and geolocation information, enabling decentralized steering policies, i.e. involving the UE and selected switches, without the SDN controller intervention. The proposal also includes an Artifical Intelligence (AI)-assisted forecast system able to predict latency and geolocation in advance and trigger faster edge steering.

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“…Along with the application of nonlinear filters designed for specific problems in telecommunications, artificial neural networks (ANNs) have been extensively studied in various challenging areas of digital communications, including soft and hard fault detection, channel estimation, equalization, and beamforming [30][31][32][33][34][35][36][37][38][39][40]. Neural networks can operate like nonlinear filters, in a structure that can be modeled by nonlinear activation functions, as in multilayer perceptrons (MLPs), or by Gaussian neurons in radial basis function neural networks (RBFNN) [35].…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

Soares

Mayer

Castro

et al. 2021

Sensors

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Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

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Soft Failure Localization Using Machine Learning with SDN-based Network-wide Telemetry

Cited by 17 publications

References 22 publications

Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Detection and Localization of Failures in Hybrid Fiber–Coaxial Network Using Big Data Platform

Extending P4 in-band telemetry to user equipment for latency- and localization-aware autonomous networking with AI forecasting

Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers

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