Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks

Gosselin, Stéphane; Courant, J.L.; Tembo, Serge Romaric; Vaton, Sandrine

doi:10.23919/ondm.2017.7958529

Cited by 15 publications

(13 citation statements)

References 9 publications

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“…Other instances of application of Bayesian models to detect and diagnose failures in optical networks, especially GPON/FTTH, are reported in [94] and [95]. In [94], the GPON/FTTH network is modeled as a Bayesian Network using a layered approach identical to one of their previous works [114].…”

Section: B Failure Managementmentioning

confidence: 99%

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An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

463

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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Section: B Failure Managementmentioning

confidence: 99%

“…Basically, the EM algorithm estimates the missing data such that the estimate maximizes the expected loglikelihood function based on a given set of parameters. In [95] a similar combination of Bayesian probabilistic models and EM is used for failure diagnosis in GPON/FTTH networks.…”

Section: B Failure Managementmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

463

226

View full text Add to dashboard Cite

show abstract

“…Cognition-based methods [97]: detects failures in centralized SDN-based networks by periodically exchanging the messages between controller and switches. Bayesian inference/networks [19], [98]: probabilistic modeling and machine learning for fault diagnosis in optical access networks…”

Section: Failure/fault Detectionmentioning

confidence: 99%

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Mata

Miguel

Durán

et al. 2018

Optical Switching and Networking

325

174

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Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

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“…BN have also been proposed for failure diagnosis in GPON/FTTH networks [52], [54], [55]. the ON is represented using a multilayer approach, where the lower layer represents the physical network topology (where nodes are ONTs and ONUs), while the middle layer models local failure propagation inside a single network component (e.g., a single node).…”

Section: E Failure Identificationmentioning

confidence: 99%

A Tutorial on Machine Learning for Failure Management in Optical Networks

Musumeci

Rottondi

Corani

et al. 2019

J. Lightwave Technol.

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Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and humandriven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

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Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks

Cited by 15 publications

References 9 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

A Tutorial on Machine Learning for Failure Management in Optical Networks

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