Paper Info
Title
Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks
Abstract
Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.
Year
DOI
Venue
2019
10.1109/TNSM.2019.2927867
IEEE Transactions on Network and Service Management
Keywords
Field
DocType
Routing,Wavelength assignment,Network topology,Approximation algorithms,Optimization
Wavelength-division multiplexing,Approximation algorithm,Computer science,Testbed,Routing and wavelength assignment,Network topology,Integer programming,Software,Optical networking,Artificial intelligence,Machine learning,Distributed computing
Journal
Volume
Issue
ISSN
16
3
1932-4537
Citations 
PageRank 
References 
3
0.38
0
Authors
8
Name
Order
Citations
PageRank
Ignacio Martin1164.40
Sebastian Troia254.12
José Alberto Hernández313124.49
Alberto Rodríguez430.38
Francesco Musumeci514824.33
Guido Maier625728.10
Rodolfo Alvizu7336.39
Óscar González De Dios87112.52