Machine Learning for DWDM Path Outage Prediction and Alternate Routing
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Solution Overview
Problem
DWDM networks experience outages due to various physical and optical issues, such as fiber problems, connector issues, and wavelength spacing, leading to degraded or blocked communications, which existing technologies struggle to predict and mitigate effectively.
Innovation Solution
A machine learning-based approach is employed to predict outages by training models using parameterized DWDM signals and network characteristics, determining network topology, and recommending alternate paths through a software-defined domain controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect DWDM outages, then the system structure remains simple, but the prediction accuracy and response time are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/optical monitoring systems with a machine learning-based predictive system. The ML model analyzes historical DWDM performance data, spectral characteristics, and network parameters to predict outages before they occur, substituting reactive monitoring with proactive prediction and significantly improving detection accuracy.
Solution Approach 2:
The system performs preliminary actions by training machine learning models with historical data and spectral characteristics in advance. The model continuously learns from past outages and performance patterns, enabling it to predict future outages before they happen, allowing the network to take preventive measures rather than reacting after failures occur.
2Loss of time
If reactive outage response is used, then the system operation is simple, but the communication disruption time is prolonged
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously receives real-time DWDM performance data, spectral measurements, and network status information. The model adjusts its predictions based on this feedback loop, and when an outage is predicted, the system automatically triggers path switching and updates its learning from the outcome, creating a closed-loop predictive system.
Solution Approach 2:
The system performs self-service by automatically detecting predicted outages, selecting alternative paths, and executing path switching without human intervention. The machine learning model autonomously analyzes data, makes predictions, and triggers remediation actions, reducing communication disruption time while managing the complexity of automated responses.
3Speed
If manual path analysis is performed, then the computational resources required are minimal, but the alternate path recommendation speed is slow
Solution Approach 1:
The patent performs preliminary computation by pre-calculating and storing spectral efficiency metrics, alternative path options, and performance characteristics during idle periods or off-peak times. When an outage is predicted, the system quickly retrieves and applies these pre-computed results, dramatically reducing recommendation speed while managing computational energy consumption through load balancing.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, predicting dense wavelength division multiplexing (DWDM) path outage prediction and recommendation of alternate communication paths. Parameterized DWDM signals are launched, and resulting network characteristics are determined. The parameters and characteristics are used to train one or more machine learning models subsequently used to predict outages and recommend alternate paths. Other embodiments are disclosed.