Aladin, Sandra et Tremblay, Christine.
20 février 2019.
« Using Artificial Intelligence in Optical Networking ».
[Article de recherche]. Substance ÉTS.
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Résumé
The increasing complexity of optical networks designed to meet a multitude of services generates massive amounts of data. In addition, any service interruption, even momentary, can cause huge data losses, leading to bad customer experience. Machine learning has been proposed and applied in several fields in the last decades. In this study, the Network Technology Laboratory is introducing a tool that estimates the quality of transmission (QoT) of lightpaths before their implementation in the network, based on machine learning algorithms. This tool could help in routing and wavelength assignment by first discarding bad QoT connections. An evaluation of three machine learning techniques for optical networks was performed: the support vector machine (SVM), the K nearest neighbours (K-NN) and the random forest (RF).
| Type de document: | Article de revue ou de magazine, non révisé par les pairs |
|---|---|
| Validation par les pairs: | Non |
| Mots-clés libres: | Machine Learning, Quality of Transmission (QoT), lightpaths, K nearest neighbors (K-NN), Random Forest (RF), Support Vector Machine (SVM) |
| Professeur: | Professeur Tremblay, Christine |
| Affiliation: | Génie électrique |
| Date de dépôt: | 26 mars 2019 16:06 |
| Dernière modification: | 19 sept. 2019 20:14 |
| URI: | https://espace2.etsmtl.ca/id/eprint/18308 |
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