Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins
This addresses operational cost reduction in optical networks by preventing costly hard-failures, though it is incremental as it applies an existing learning framework to a specific domain problem.
The paper tackles the problem of predicting soft-failure evolution in optical networks to trigger timely repairs, showing that the proposed encoder-decoder framework can predict failures several days in advance, reducing premature or late repairs compared to rule-based methods.
In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon. This enables the triggering of timely repair actions with low quality-of-transmission (QoT) margins before a costly hard-failure occurs, ultimately reducing the frequency of repair actions and associated operational expenses. Specifically, it is shown that the proposed scheme is capable of triggering a repair action several days prior to the expected day of a hard-failure, contrary to soft-failure detection schemes utilizing rule-based fixed QoT margins, that may lead either to premature repair actions (i.e., several months before the event of a hard-failure) or to repair actions that are taken too late (i.e., after the hard failure has occurred). Both frameworks are evaluated and compared for a lightpath established in an elastic optical network, where soft-failure evolution can be modeled by analyzing bit-error-rate information monitored at the coherent receivers.