Network Telemetry Analysis for ML-Based Impairment Localization
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Solution Overview
Problem
Existing techniques for identifying and localizing impaired network components in data networks are unreliable, inaccurate, and fail to prioritize repairs based on degradation severity and customer impact, often leading to inefficient troubleshooting.
Innovation Solution
A method involving the creation of controlled network conditions in a laboratory environment using Faraday cages to simulate noise and impairments, allowing for precise data collection and labeling, which is used to train machine learning models to identify and localize network issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional noise identification techniques are used in operational networks, then service continuity is maintained, but identification accuracy and localization precision deteriorate
Solution Approach 1:
The patent applies preliminary action by creating controlled impairment scenarios in a laboratory environment before deploying detection algorithms to operational networks. Training data is generated in advance under known conditions, allowing machine learning models to be pre-trained and validated, thereby achieving high identification accuracy without disrupting actual network service when deployed.
2Measurement precision
If comprehensive training data is collected from operational networks, then model accuracy improves, but data quality and labeling reliability deteriorate
Solution Approach 1:
The patent extracts the data collection and labeling process from the operational network environment to a controlled laboratory setting. By taking out the impairment introduction process from live networks, the system can generate training data with known ground truth labels, eliminating the ambiguity and reliability issues associated with labeling data from operational environments while maintaining model training accuracy.
Solution Approach 2:
The patent creates copies of operational network topologies and components in a laboratory environment. These replicated networks mimic real-world conditions but allow for controlled introduction of impairments. The copying approach enables generation of training data that reflects operational scenarios while maintaining precise control over impairment characteristics and accurate labeling.
3Productivity
If machine learning models are trained with simulated data, then identification efficiency improves, but adaptability to real-world variations deteriorates
Solution Approach 1:
The patent applies local quality by creating diverse, targeted impairment scenarios in the laboratory that specifically address different types of network failures (e.g., cable faults, connector issues, amplifier problems). Each simulated scenario focuses on specific local characteristics of real-world impairments, allowing the model to learn detailed patterns for different failure modes while maintaining overall adaptability through comprehensive scenario coverage.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying impairment characteristics in simulated scenarios, including different noise levels, signal attenuation values, and component failure modes. By changing these parameters across multiple training scenarios, the model learns to adapt to a wide range of real-world conditions while maintaining high troubleshooting efficiency through structured simulation data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of comprehensive training data for machine learning algorithms, enhancing the accuracy and efficiency of identifying and addressing network impairments, thereby improving service quality and reducing customer dissatisfaction.
Implementation Method 1
A method involving the creation of controlled network conditions in a laboratory environment using Faraday cages to simulate noise and impairments
Data Source
AI summary
Various techniques for analyzing data networks with impaired network components, recording network telemetry data and labeling the information for use in training one or more machine learning algorithms is described. The machine learning algorithms trained in this way are used to detect impaired network components and/or noise on operational data networks.