Telecommunication Network Issue Prediction and Resolution
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Solution Overview
Problem
Complex telecommunication networks face challenges in predicting and resolving issues due to their intricate interdependencies, making it difficult to pinpoint and address problems before they propagate unpredictably across the network.
Innovation Solution
A system that builds a service registry to store dependence information, uses machine learning models to detect errors by analyzing logs, and automatically predicts and resolves issues by determining similar past issues and applying corresponding solutions, notifying dependent components and administrators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models analyze logs to detect errors in complex telecommunication networks, then issue detection capability is improved, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that analyze network logs and detect errors. These models serve as mediators between raw network data and issue identification, transforming complex log analysis into actionable error detections without requiring direct human intervention in the complexity of network interdependencies.
Solution Approach 2:
The patent replaces manual or rule-based error detection mechanisms with machine learning models. This substitution allows the system to automatically learn patterns from network logs and identify issues based on learned behaviors rather than predetermined rules, improving detection capability while managing complexity through automation.
2Productivity
If the system automatically resolves issues by propagating fixes across interdependent components, then productivity is improved, but reliability may worsen due to unpredictable propagation
Solution Approach 1:
The patent builds a service registry that stores dependence information about network components in advance. This preliminary structuring of component relationships allows the system to understand propagation paths before issues occur, enabling more controlled and reliable automatic resolution by anticipating how fixes will propagate through the network.
Solution Approach 2:
The system uses machine learning models to analyze network logs and detect errors, creating a feedback loop that continuously monitors network health. This feedback mechanism allows the system to learn from past issues and improve its automatic resolution capabilities, adjusting its behavior based on observed outcomes to maintain reliability while improving productivity.
Data Source
AI summary
Disclosed here is a system to automatically predict and resolve issues within a telecommunication network. Initially, the system builds a service registry to store dependence information within the network, which can include software components and hardware components. Various components of the network create logs of their operations. Machine learning models examine the logs and detect any issues. Upon detecting an issue or abnormal event, the system can automatically resolve the issue by determining the most similar issue occurring previously and determining a solution that resolved the previous most similar issue. In addition, the system can propagate the fix to dependent systems and/or notify the dependent systems of the issue.