Scalability analysis of machine learning QoT estimators for a cloud-native SDN controller on a WDM over SDM network
Por:
Manso, C., Vilalta, R., Munoz, R., Yoshikane, N., Casellas, R., Martinez, R., Wang, C., Balasis, F., Tsuritani, T., Morita, I.
Publicada:
1 ene 2022
Categoría:
Computer networks and communications
Resumen:
Maintaining a good quality of transmission (QoT) in optical transport networks is key to maintaining the service level agreement between the user and the service provider. QoT prediction techniques have been used to assure the quality of new lightpaths as well as that of the previously provisioned ones. Traditionally, two different approaches have been used: analytical methods, which take into account most physical impairments that are accurate but complex, and high margin formulas, which require much less computational resources at the cost of high margins. With the recent progress of machine learning (ML) together with software defined networking (SDN), ML has been considered as another option that could be both accurate and that does not consume as many resources as analytical methods. SDN architectures are difficult to scale because they are usually centralized; this is even worse with QoT predictors using ML. In this paper, a solution to this issue is presented using a cloud-native architecture, and its scalability is evaluated using three different ML QoT predictors and experimentally validated in a real wavelength-division multiplexing (WDM) over spatial-division multiplexing (SDM) testbed. © 2009-2012 Optica Publishing Group.
Filiaciones:
Manso, C.:
Optical Networks and Systems Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Spain
Vilalta, R.:
Optical Networks and Systems Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Spain
Munoz, R.:
Optical Networks and Systems Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Spain
Yoshikane, N.:
KDDI Research Inc., 2-1-15 Ohara, Fujimino-shi, Saitama, 356-8502, Japan
Casellas, R.:
Optical Networks and Systems Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Spain
Martinez, R.:
Optical Networks and Systems Department, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, 08860, Spain
Wang, C.:
KDDI Research Inc., 2-1-15 Ohara, Fujimino-shi, Saitama, 356-8502, Japan
Balasis, F.:
KDDI Research Inc., 2-1-15 Ohara, Fujimino-shi, Saitama, 356-8502, Japan
Tsuritani, T.:
KDDI Research Inc., 2-1-15 Ohara, Fujimino-shi, Saitama, 356-8502, Japan
Morita, I.:
KDDI Research Inc., 2-1-15 Ohara, Fujimino-shi, Saitama, 356-8502, Japan
Green Published, All Open Access; Green Open Access
|