Graph wavelets for fault localization in optical mesh networks

Abstract

• Real-time fault detection and localization mechanism in Elastic Optical Networks (EONs). • Alarm correlation strategies fail in the face of static threshold settings. • Detects and localizes silent network failures. • Multi-resolution analysis based on graph wavelets to address fault detection and localization problems. • Exceeds alarm based engines for fault detection and localization accuracy. EONs (Elastic Optical Networks) form the backbone of web-scale cloud infrastructure. These networks should deal with network faults and restoration in real-time with high accuracy and scalability. Thus, fault management functions to detect and localize such failures, play a critical role in modern day EONs. Network impairments are complex in nature in optical networks such as fiber losses, NLI (Non Linear Impairments) and ASE (Amplified Stimulated Emission). Currently, pre-engineered thresholds are used to detect failures in optical networks. Traditional alarm correlation strategies fail in the face of these static threshold settings. In this work, we introduce a real-time fault detection and localization mechanism, which is agnostic to such pre-engineered threshold settings. We apply multi-resolution analysis based on graph wavelets to address fault detection and localization problems. Thus, we propose to use spectrograms and wavelet coefficients to identify the irregularities in an otherwise smooth graph signal. We present a real-time fault localization analysis based on a novel Wavelet Coefficients Trail (WCT) algorithm to root-cause a client signal failure due to OSNR (Optical Signal to Noise Ratio) degradation. We apply the proposed techniques and algorithms to simulated and actual network data. Our results indicate that the proposed methods can root-cause OSNR degradation to major/minor deviations in received power on optical spans. In conclusion, we present a fault localization framework based on graph wavelets in optical mesh networks.