Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins

TL;DR

This paper presents an encoder-decoder learning framework that predicts soft-failure evolution in optical networks, enabling timely repairs days before hard failures occur, thus reducing operational costs and avoiding premature or late interventions.

Contribution

It introduces a novel machine learning approach for long-term soft-failure prediction in optical networks, improving repair timing over traditional rule-based methods.

Findings

The framework can trigger repairs several days before hard failures.

It outperforms rule-based fixed QoT margin schemes.

It effectively reduces repair frequency and operational expenses.

Abstract

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.