Building Autonomic Optical Whitebox-Based Networks

Velasco, Luis; Sgambelluri, Andrea; Casellas, Ramon; Gifre, Lluís; Izquierdo-Zaragoza, Jose-Luis; Fresi, Francesco; Paolucci, F.; Martínez, Ricardo; Riccardi, E.

doi:10.1109/jlt.2018.2829700

Cited by 47 publications

(39 citation statements)

References 12 publications

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“…Aiming at emulating failure scenarios, we modify the characteristics of the 2 nd WSS of each node (from N1 to N8) in the set-up; its bandwidth and central frequency are modified to model FT and FS failures, respectively. A large dataset of failures was collected by inducing failures of magnitude in the range [1][2][3][4][5][6][7][8] GHz for FS and in the range [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] GHz for FT, both with 0.25 GHz step-size, where the magnitude of FT is defined as the difference between the ideal bandwidth of the filter (37.5 GHz) and its actual bandwidth during the failure.…”

Section: A Vpi Set-up For Data Collectionmentioning

confidence: 99%

“…Following such idea, the authors in [9] and [10] proposed a distributed Monitoring and Data Analytics (MDA) framework that includes MDA agents running close to the observation points in the network nodes, as well as a centralized MDA controller running in the control and management plane besides the Software Defined Networking (SDN) controller. Such MDA framework is the base to build autonomic optical networks, especially in the case of utilizing white boxes which might include specific optical monitoring devices [11].…”

Section: Introductionmentioning

confidence: 99%

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Learning From the Optical Spectrum: Failure Detection and Identification

Shariati

Ruiz

Comellas

et al. 2019

J. Lightwave Technol.

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The availability of coarse-resolution cost-effective Optical Spectrum Analyzers (OSA) allows its widespread deployment in operators' networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures; filter shift and tight filtering which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are: i) multi-classifier approach, which uses features extracted directly from the optical spectrum, ii) single-classifier approach, which uses pre-processed features to compensate for filter cascading, and iii) residual-based approach, which uses a residual signal computed from subtracting the signal acquired by OSAs from an expected signal synthetically generated. Extensive numerical results are ultimately presented to compare the performance of the proposed approaches in terms of accuracy and robustness.

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Section: A Vpi Set-up For Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning From the Optical Spectrum: Failure Detection and Identification

Shariati

Ruiz

Comellas

et al. 2019

J. Lightwave Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…To overcome these problems, the distributed architecture (Fig. 2b) includes MDA agents in charge of collecting measurements from a single node, while keeping the MDA controller centralized [12,13]. The MDA agent exposes two unified interfaces toward the MDA controller for collecting data and monitoring the configuration.…”

Section: Mda Architecturesmentioning

confidence: 99%

“…The distributed architecture includes a dedicated MDA agent for every node in the network, which might present some limitations when disaggregated optical network nodes (e.g., TPs and [13]. For this reason, the hierarchical architecture (Fig.…”

Section: Mda Architecturesmentioning

confidence: 99%

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Velasco

Ruiz²,

Cugini

et al. 2019

IEEE Network

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“…Behind the autonomic concept, machine learning (ML) plays an essential role for a wide range of use cases in optical networks (see [3][4][5][6][7]). Examples include use cases from self-configuration to predictive maintenance and, at several levels, from transmission to single and multilayer network [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

Velasco¹,

Shariati²,

Boitier

et al. 2019

J. Opt. Commun. Netw.

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Autonomic optical transmission and networking requires machine learning (ML) models to be trained with large datasets. However, the availability of enough real data to produce accurate ML models is rarely ensured since new optical equipment and techniques are continuously being deployed in the network. One option is to generate data from simulations and lab experiments, but such data could not cover the whole features space and would translate into inaccuracies in the ML models. In this paper, we propose an ML-based algorithm life cycle to facilitate ML deployment in real operator networks. The dataset for ML training can be initially populated based on the results from simulations and lab experiments. Once ML models are generated, ML retraining can be performed after inaccuracies are detected to improve their precision. Illustrative numerical results show the benefits of the proposed learning cycle for general use cases. In addition, two specific use cases are proposed and demonstrated that implement different learning strategies: (i) a two-phase strategy performing out-of-field training using data from simulations and lab experiments with generic equipment, followed by an in-field adaptation to support heterogeneous equipment (the accuracy of this strategy is shown for a use case of failure detection and identification), and (ii) in-field retraining, where ML models are retrained after detecting model inaccuracies. Different approaches are analyzed and evaluated for a use case of autonomic transmission, where results show the significant benefits of collective learning.

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Building Autonomic Optical Whitebox-Based Networks

Cited by 47 publications

References 12 publications

Learning From the Optical Spectrum: Failure Detection and Identification

Learning From the Optical Spectrum: Failure Detection and Identification

Monitoring and Data Analytics for Optical Networking: Benefits, Architectures, and Use Cases

Learning Life Cycle to Speed Up Autonomic Optical Transmission and Networking Adoption

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