Cognitive Assurance Architecture for Optical Network Fault Management

Rafique, Danish; Szyrkowiec, Thomas; Grieser, Helmut; Autenrieth, Achim; Elbers, Jörg-Peter

doi:10.1109/jlt.2017.2781540

Cited by 95 publications

(47 citation statements)

References 17 publications

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“…However, real environment is usually time-sensitive, and a better comparison principle is to compare the suboptimal solutions of different algorithms under a given time threshold. [104]. ii) In decision-making tasks, suboptimal solutions should be allowed to balance the network performance and time consuming of action computation.…”

Section: Algorithm Computational Complexitymentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

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With the rapid development of Internet and communication systems, both in services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML).Optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems; and thus recently, ML techniques have been used for many optical network applications.In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical networks monitoring and survivability. The use cases are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.

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Section: Algorithm Computational Complexitymentioning

confidence: 99%

Machine learning for intelligent optical networks: A comprehensive survey

Yang

2020

Journal of Network and Computer Applications

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“…Alternatively to BNs, frameworks incorporating multiple ML algorithms have been proposed, where each algorithm focuses on a specific task (e.g., the identification of a particular category of failures), as in [34] and [46], [47] where ANNs are used to perform failure identification in controlled ON testbeds. In [61], the output of the BER anomaly detection mentioned in Section IV-B is fed into a probabilistic algorithm together with historical BER time series, which then returns the most probable failure type from a predefined set of possible causes.…”

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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“…Early detection of lightpaths' degradation would allow tuning parameters within the TPs, for example, by increasing the FEC overhead or by switching to a more robust modulation format [5]. When the severity of the degradation increases, localizing its root cause is of paramount importance for maintenance purposes [6,7]. It is also possible to predict failures and proactively re-route the traffic [8], which allows a high resiliency of the optical network at the just-enough cost.…”

Section: Operators' Vision In Near-term and Data Availability The Netmentioning

confidence: 99%

“…To cope with complex and time-variable 5G service scenarios, Machine Learning (ML)-based algorithms [2] are being proposed to facilitate network operation and predictive maintenance. ML algorithms, fed with real measurements, are able to accurately estimate the Quality of Transmission (QoT) of new lightpaths, to anticipate capacity exhaustion and degradations, or to predict and localize failures, among others (see, e.g., [3][4][5][6][7][8]).…”

Section: Introductionmentioning

confidence: 99%