Considering flexible technologies available nowadays, operating optical networks much closer to their physical capacities is very tempting but necessarily requires efficient network automation. To achieve this, the two main challenges are handling failures, and accurately predicting performance in dynamic environments. We experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to preventively mitigate their impact, and thus guarantee smooth network operation. Then, leveraging machine learning for live performance estimation and closed-loop software-defined network control, we demonstrate a fully automated reconfiguration of marginless connections undergoing critical performance variations over 228 km of field-deployed fiber.
Marginless operation of optical networks
Sgambelluri A.;Sambo N.;Castoldi P.;
2019-01-01
Abstract
Considering flexible technologies available nowadays, operating optical networks much closer to their physical capacities is very tempting but necessarily requires efficient network automation. To achieve this, the two main challenges are handling failures, and accurately predicting performance in dynamic environments. We experimentally demonstrate the ability of the ORCHESTRA solution for early detection and localization of failures, to preventively mitigate their impact, and thus guarantee smooth network operation. Then, leveraging machine learning for live performance estimation and closed-loop software-defined network control, we demonstrate a fully automated reconfiguration of marginless connections undergoing critical performance variations over 228 km of field-deployed fiber.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
