Failure Management Overview in Optical Networks
Conventional optical networks are limited by static operational methods that hinder their scalability and effectiveness. As networks operate with reduced margins to maximize resource utilization, the risk of hard failures increases, necessitating efficient failure prediction systems and accurate qua...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10752984/ |
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| author | Sergio Cruzes |
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| collection | DOAJ |
| description | Conventional optical networks are limited by static operational methods that hinder their scalability and effectiveness. As networks operate with reduced margins to maximize resource utilization, the risk of hard failures increases, necessitating efficient failure prediction systems and accurate quality of transmission (QoT) estimation. Effective management requires the detection of soft failures, accurate bit error rate (BER) predictions, and dynamic network operations to maintain minimal margins. Machine learning (ML) offers promising solutions for automating these tasks, significantly enhancing failure management and network reliability. This article provides an extensive overview of ML techniques applied to optical networks, specifically focusing on failure management. The key ML techniques discussed include network kriging (NK) for performance estimation and failure localization, support vector machine (SVM) for classification tasks, convolutional neural networks (CNNs) for signal analysis and soft failure identification, and generative adversarial networks (GANs) for synthetic data generation and soft failure detection. It also explores the application of artificial neural networks (ANNs), autoencoders (AEs), Gaussian process (GP), long short-term memory (LSTM), and gated recurrent units (GRUs) in optical networks. This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. Emphasizing automation, it discusses how ML algorithms can streamline failure management processes, reducing manual intervention and service disruptions. The potential of large language models (LLMs) and digital twins (DTs) for further advancements in automating failure management, optimizing performance, and network optimization in optical networks is also examined. LLMs significantly advance network management by improving network design, diagnosis, security, and autonomous optimization through the integration of comprehensive domain resources and intelligent agents. These advancements are paving the way towards achieving artificial general intelligence and fully automated optical network management. |
| format | Article |
| id | doaj-art-97ce5c12c0c4472cbb34660f8b57cc42 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-97ce5c12c0c4472cbb34660f8b57cc422025-08-20T02:32:45ZengIEEEIEEE Access2169-35362024-01-011216917016919310.1109/ACCESS.2024.349870410752984Failure Management Overview in Optical NetworksSergio Cruzes0https://orcid.org/0009-0008-8955-7628Department of Optical Network Engineering, Ciena Corporation, Ciena Office Brazil, São Paulo, BrazilConventional optical networks are limited by static operational methods that hinder their scalability and effectiveness. As networks operate with reduced margins to maximize resource utilization, the risk of hard failures increases, necessitating efficient failure prediction systems and accurate quality of transmission (QoT) estimation. Effective management requires the detection of soft failures, accurate bit error rate (BER) predictions, and dynamic network operations to maintain minimal margins. Machine learning (ML) offers promising solutions for automating these tasks, significantly enhancing failure management and network reliability. This article provides an extensive overview of ML techniques applied to optical networks, specifically focusing on failure management. The key ML techniques discussed include network kriging (NK) for performance estimation and failure localization, support vector machine (SVM) for classification tasks, convolutional neural networks (CNNs) for signal analysis and soft failure identification, and generative adversarial networks (GANs) for synthetic data generation and soft failure detection. It also explores the application of artificial neural networks (ANNs), autoencoders (AEs), Gaussian process (GP), long short-term memory (LSTM), and gated recurrent units (GRUs) in optical networks. This study surveys ML techniques for early-warning and failure prediction, failure detection, identification, localization, magnitude estimation, and soft failure detection and prediction. Emphasizing automation, it discusses how ML algorithms can streamline failure management processes, reducing manual intervention and service disruptions. The potential of large language models (LLMs) and digital twins (DTs) for further advancements in automating failure management, optimizing performance, and network optimization in optical networks is also examined. LLMs significantly advance network management by improving network design, diagnosis, security, and autonomous optimization through the integration of comprehensive domain resources and intelligent agents. These advancements are paving the way towards achieving artificial general intelligence and fully automated optical network management.https://ieeexplore.ieee.org/document/10752984/Optical networksfailure managementquality of transmissionmachine learning |
| spellingShingle | Sergio Cruzes Failure Management Overview in Optical Networks IEEE Access Optical networks failure management quality of transmission machine learning |
| title | Failure Management Overview in Optical Networks |
| title_full | Failure Management Overview in Optical Networks |
| title_fullStr | Failure Management Overview in Optical Networks |
| title_full_unstemmed | Failure Management Overview in Optical Networks |
| title_short | Failure Management Overview in Optical Networks |
| title_sort | failure management overview in optical networks |
| topic | Optical networks failure management quality of transmission machine learning |
| url | https://ieeexplore.ieee.org/document/10752984/ |
| work_keys_str_mv | AT sergiocruzes failuremanagementoverviewinopticalnetworks |