Elastic Optical Networks Based Optimization Using Machine Learning: State-Of-Art Review
Keywords:
Elastic optical networks, data transmission line, link monitoring, Machine learning, link provisioningAbstract
To address the issues and difficulties relating to network capacity, elastic optical networks have been developed. Elastic optical networks have been regarded as being primarily enabled by software and hardware resource optimization (EONs). Several Tb/s long-distance data transmission lines can benefit from elastic optical networks' improved spectral efficiency. Elastic optical networks now use machine learning (ML) approaches to facilitate automated processes and unlock more physical layer capacity. One drawback is that training environment—such as traffic pattern or structure of optical network—has a significant impact on how much is learned. Retraining is therefore necessary in cases of network topology or traffic pattern changes, which requires a lot of processing power and time. We examine and debate several ML applications in connection modelling, provisioning, and network control.
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