A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci, Francesco; Rottondi, Cristina; Corani, Giorgio; Shahkarami, Shahin; Cugini, F.; Tornatore, Massimo

doi:10.1109/jlt.2019.2922586

Cited by 103 publications

(61 citation statements)

References 49 publications

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“…The data is not labeled beforehand, but UL performs density-based clustering to analyze patterns in the monitored data which are then further analyzed with an SL module. An encompassing tutorial on ML for failure management is presented in [22].…”

Section: A Autonomous Optical Network Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

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Section: A Autonomous Optical Network Managementmentioning

confidence: 99%

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Furdek

Natalino

Lipp³

et al. 2020

J. Lightwave Technol.

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“…Therefore, a reasonable adaptation scheme is also needed after deployment. In EON, online learning approaches such as retraining are preferable to cope with time-evolving network scenarios [73]. Even though collecting data from the practical system for retraining has been proposed in many works, the rationality for the retraining scheme needs to be reconsidered.…”

Section: Future Workmentioning

confidence: 99%

mentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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“…Finally, the talk discussed new possible research directions in the field. The talk was based on a recent survey [1] and a recent tutorial, [2] published by the speaker. Following the short introduction, she presented the results of the group's recent activities in the area of phase noise characterization for lasers and frequency combs [3,4], Raman amplifier inverse design and modelling [5][6][7], and auto-encoders for optical communication systems [8][9][10].…”

Section: Overviewmentioning

confidence: 99%

Summary:

Gordon¹,

Battou²,

Majurski³

et al. 2020

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Optical communication systems are expected to find use in new applications that require more intelligent and automated functionality. Optical networks are needed to address the high speeds and low latency of 5G wireless networks. The analog nature of optical transmission and the complexity of operation and management remain an impediment to greater use of software controls. The optical community at large has proposed many possible applications and avenues for using and implementing artificial intelligence and machine learning to improve functionality of optical systems for communications. However, broad agreement has yet to be reached due to both technical and non-technical reasons. On August 2 nd , 2019 The National Institute of Standards and Technology (NIST) Communications Technology Laboratory (CTL) hosted a Workshop on Machine Learning for Optical Communication Systems to bring together industry, academia and government in order to discuss the roll of AI and ML in optical communication systems. This document provides an overview and summary of the workshop.

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A Tutorial on Machine Learning for Failure Management in Optical Networks

Cited by 103 publications

References 49 publications

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

Machine Learning for Optical Network Security Monitoring: A Practical Perspective

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

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