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    Area of Science:

    • Telecommunications Engineering
    • Network Management
    • Machine Learning Applications

    Background:

    • Optical networks face challenges balancing service capability with rising operational expenses (OPEX) for operations, administration, and maintenance (OAM).
    • Machine learning (ML) offers potential for intelligent automation in OAM due to its feature extraction capabilities.
    • Integrating ML into optical networks requires significant data storage and computing resources, posing architectural challenges.

    Purpose of the Study:

    • To propose a novel optical network architecture for intelligent OAM.
    • To address the integration challenges of ML resources in optical networks.
    • To introduce the self-optimizing optical networks (SOON) architecture based on software-defined networking (SDN).

    Main Methods:

    • Literature review on the intelligence development of optical networks.
    • Introduction of the SOON architecture, designed for OAM.
    • Demonstration of four key applications within the SOON framework.

    Main Results:

    • SOON architecture effectively integrates ML capabilities for enhanced optical network OAM.
    • Demonstrated applications include tidal traffic prediction, alarm prediction, anomaly detection, and routing/wavelength assignment.
    • The proposed architecture addresses the need for robust data storage and computing for ML in optical networks.

    Conclusions:

    • The SOON architecture, based on SDN, provides a viable solution for intelligent OAM in optical networks.
    • ML integration within SOON can significantly improve network performance and reduce operational costs.
    • Further research is needed to address open issues in ML-enabled optical network architectures.