Engineering topics
Samadi, Payman
Publications and source records attributed to Samadi, Payman.
Autonomous Network and IT Resource Management for Geographically Distributed Data Centers
Not Available
Power Excursion Mitigation for Flexgrid Defragmentation With Machine Learning
Flexgrid optical networking relies on spectrum defragmentation to groom channel wavelengths and improve spectral efficiency. Various defragmentation methods—hop, make-before-break, and sweep—interact with the power dynamics of erbium-doped fiber amplifiers (EDFA) differently and result in undesired power excursions that exacerbate the post-EDFA power variance. Here we present a machine learning engine that characterizes the channel dependence of power excursions from historical data. We further demonstrate that post-EDFA power variance during hop, make-before-break, and sweep defragmentation methods can be greatly mitigated by the trained machine learning engine with automated and expedited power adjustment predictions.
Flexible Network Architecture and Provisioning Strategy for Geographically Distributed Metro Data Centers
Not Available
Dynamic mitigation of EDFA power excursions with machine learning
Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize and mitigate the power excursions of EDFA systems with different equipment and scales. An ML engine is developed and experimentally validated to show accurate predictions of the power dynamics in cascaded EDFAs. Recommended channel provisioning based on the ML predictions achieves within 1% error of the lowest possible power excursion over 94% of the time. In conclusion, we also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm.
Flexfly: Enabling a Reconfigurable Dragonfly through Silicon Photonics.
Abstract not provided.