Machine Learning Network Fault Recovery for Root Cause Delays
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Solution Overview
Problem
Complex wireless networks face challenges in quickly identifying and resolving system-level experience issues due to the complexity of factors involved, leading to substantial time in determining root causes, which can impact users significantly when the system is inoperative or operating at reduced capacity.
Innovation Solution
A machine learning model is employed to monitor operational parameters and messages within the network, identify faults, and select corrective actions based on probabilities and costs, iterating until the system achieves nominal performance, with human support for training and automated defect reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify root causes in complex wireless networks, then diagnostic accuracy can be maintained through human expertise, but the time required to resolve faults increases substantially
Solution Approach 1:
The patent replaces manual diagnostic processes with an automated machine learning model that analyzes network data to identify root causes. The system uses automated data collection, processing, and analysis mechanisms to substitute human expertise, achieving both speed and accuracy through algorithmic decision-making rather than manual investigation.
Solution Approach 2:
The system enables self-diagnosis and self-resolution of network faults through automated machine learning models that independently analyze network data, identify problems, and recommend or implement corrective actions without requiring human intervention for each fault event.
2Measurement precision
If the network system performs comprehensive diagnostics to ensure accurate fault identification, then diagnostic precision improves, but system productivity decreases due to extended downtime
Solution Approach 1:
The system continuously collects and pre-processes network data in the background during normal operation, building knowledge bases and training machine learning models proactively. When faults occur, the pre-prepared diagnostic capabilities enable immediate analysis without adding to the fault resolution time, thus maintaining both accuracy and productivity.
Solution Approach 2:
Automated machine learning models perform comprehensive diagnostics instantly by analyzing pre-collected network data, replacing the time-consuming manual diagnostic process. The system evaluates multiple potential root causes simultaneously through algorithmic processing, achieving high diagnostic accuracy without extending system downtime.
3Device complexity
If manual diagnostic processes are used, then system complexity can be managed through human judgment, but the ease of operation deteriorates due to the substantial time and effort required
Solution Approach 1:
The system automates the entire diagnostic and resolution process, allowing the network infrastructure to self-diagnose and self-correct faults. The machine learning model independently navigates the complex diagnostic space, eliminating the need for human operators to manually navigate complex troubleshooting procedures.
Solution Approach 2:
The patent introduces an automated machine learning intermediary that mediates between the complex network system and the user. This intermediary handles the complexity of diagnostic analysis, data interpretation, and corrective action selection, presenting simplified fault resolution to users without exposing them to the underlying system complexity.
Data Source
AI summary
Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.