Journal Article10.1007/978-981-97-0892-5_20
Machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks
Saloni Rai,Amit Garg +1 more
- 01 Jan 2024
- pp 251-265
About: The article was published on 01 Jan 2024. The article focuses on the topics: Computer science & Extraction (chemistry).
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References
An Overview on Application of Machine Learning Techniques in Optical Networks
Francesco Musumeci,Cristina Rottondi,Avishek Nag,Irene Macaluso,Darko Zibar,Marco Ruffini,Massimo Tornatore +6 more
TL;DR: An overview of the application of ML to optical communications and networking is provided, relevant literature is classified and surveyed, and an introductory tutorial on ML is provided for researchers and practitioners interested in this field.
Artificial intelligence (AI) methods in optical networks: A comprehensive survey
Javier Mata,Ignacio de Miguel,Ramón J. Durán,Noemí Merayo,Sandeep Singh,Admela Jukan,Mohit Chamania +6 more
TL;DR: A comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks and a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.
353
Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction
Vicente Alarcon-Aquino,Javier A. Barria +1 more
- 01 Mar 2006
TL;DR: The proposed multiresolution FIR neural-network-based learning algorithm is applied to network traffic prediction (real-world aggregate Ethernet traffic data) with comparable results, indicating that the generalization ability of the FIR neural network is improved by the proposedMultiresolution learning algorithm.
Defragmentation Scheme Based on Exchanging Primary and Backup Paths in 1+1 Path Protected Elastic Optical Networks
TL;DR: This paper proposes a defragmentation scheme using path exchanging in 1+1 path protected EONs, and proves that a decision version of the defined static reallocation problem is NP-complete.
63
Traffic prediction based on machine learning for elastic optical networks
TL;DR: Two approaches that employ the machine learning techniques to enable traffic prediction in Elastic Optical Networks are presented and results show that the application of adaptive strategies has superior performance.
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