Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration

Abstract Optical transport networks rely on reactive fault management, which guarantees service disruption during the onset of soft failures. We present a framework for proactive soft-failure prediction that combines physics-inspired feature engineering, tree-ensemble machine learning, and Infrastructure-as-Code (IaC) orchestration. The framework is validated on (i) a multi-physics stochastic simulation spanning five degradation modes (Ornstein–Uhlenbeck, exponential, Weibull, step, oscillatory) and (ii) a publicly available real optical telemetry benchmark (Ghosh machine-learning inference contributes < 0.5%. The framework is scoped to gradual OSNR-degrading failures (EDFA pump aging, nonlinear-interference drift); extension to laser-current-visible ECL failures through multi-channel feature fusion is identified as future work.