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Aladin, Sandra and Tremblay, Christine.
20 February 2019.
« Using Artificial Intelligence in Optical Networking ».
[Research article]. Substance ÉTS.
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Abstract
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).
| Item Type: | Non-peer reviewed article published in a journal or magazine |
|---|---|
| Refereed: | No |
| Uncontrolled Keywords: | Machine Learning, Quality of Transmission (QoT), lightpaths, K nearest neighbors (K-NN), Random Forest (RF), Support Vector Machine (SVM) |
| Professor: | Professor Tremblay, Christine |
| Affiliation: | Génie électrique |
| Date Deposited: | 26 Mar 2019 16:06 |
| Last Modified: | 19 Sep 2019 20:14 |
| URI: | https://espace2.etsmtl.ca/id/eprint/18308 |
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