Experimental Demonstration of Online Learning-Based Concept Drift Adaptation for Failure Detection in Optical Networks

TL;DR

This paper introduces an online learning method for adapting to concept drift in optical network failure detection, significantly improving performance and maintaining low latency.

Contribution

It presents a novel online learning approach specifically designed for concept drift adaptation in optical network failure detection.

Findings

Achieved up to 70% performance improvement over static models.

Maintained low latency in failure detection.

Demonstrated effectiveness in real-world optical networks.

Abstract

We present a novel online learning-based approach for concept drift adaptation in optical network failure detection, achieving up to a 70% improvement in performance over conventional static models while maintaining low latency.