3 papers indexed
Omran Ayoub, Carlos Natalino, Ali Al Housseini, Felix Foschum, Philipp Morger, Tiziano Leidi, et al.
Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource allocation. In these environments, ML-driven predictions may be directly coupled with control-plane…
Ali Al Housseini, Carlos Natalino, Paolo Monti, Omran Ayoub
The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or operational configuration often exhibit degraded performance when deployed in unseen networks. In t…
Kiarash Rezaei, Omran Ayoub, Paolo Monti, Carlos Natalino
Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuGLEN, a stepwise evaluation pipeline that uses an LLM-as-a-judge together with a small set of expert…