Effective fault management is essential for quality of service assurance in optical networks.
Conventional fault management designs for optical networks mainly rely on threshold-based …

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) …

Optical network failure management (ONFM) is a promising application of machine learning
(ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored …

In face of staggering traffic growth driven by cloud-based platforms, modern optical networks—
forming the backbone of such connectivity—are faced with increasing requirements in terms …

Failure management plays a significant role in optical networks. It ensures secure operation,
mitigates potential risks, and executes proactive protection. Machine learning (ML) is …

The deployment of 5G and network slicing has challenged the current network management
requirements, triggering the need for programmable and software-driven architectures …

The increase in computing capacity and the huge amount of available data have
significantly accelerated the use of artificial intelligence and machine learning algorithms to …

This paper presents a hybrid learning approach for cooperative fault management in multi-
domain optical networks (MD-ONs). The proposed approach relies on a broker-based MD …

Machine-Learning-Based Soft-Failure Detection and Identification in Optical Networks Page 1
M3A.5.pdf OFC 2018 © OSA 2018 Machine-Learning-Based Soft-Failure Detection and …

To support the pervasive digital evolution, optical network infrastructures must be able to
quickly and effectively adapt to changes arising from traffic dynamicity or external factors …