Abstract
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. We also showcase significant mitigation of EDFA power excursions in super-channel provisioning when compared to the first-fit wavelength assignment algorithm.
Highlights
Dynamic workloads such as video streaming, Internet of Things (IoT), and cloud computing require optical networks to handle growing traffic demands with agility and resilience to faults [1,2]
We demonstrated that the machine learning (ML) approach is directly transferrable among erbium doped fiber amplifiers (EDFA) systems with different equipment and scales
We introduce an ML engine based on a regression approach that characterizes the channel dependence of power excursions in a WDM network with multiple cascaded EDFAs
Summary
Dynamic workloads such as video streaming, Internet of Things (IoT), and cloud computing require optical networks to handle growing traffic demands with agility and resilience to faults [1,2]. The added variability of channel bandwidth in flexgrid networks means that EDFA systems need to respond to a variety of spectral power changes [6]. In contrast to a fixed, closed-form analytical model of the system, the ML approach flexibly adapts to the historical responses of the amplifiers by performing regression on the selection of channels and the discrepancy of their post-EDFA power levels. We extend the work in [16] and present (i) two ML models supported by the ML engine that characterize EDFA power responses, (ii) an extension of the ML-based approach to provision WDM channels with variable spectral bandwidth to support flexgrid networks, and (iii) a thorough analysis of the performance of our proposed models in singleand super-channel provisioning scenarios.
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