Automated Root Cause Analysis for Telecommunications Networks
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Solution Overview
Problem
The complexity of telecommunications network systems requires efficient troubleshooting processes to reduce mean time to repair (MTTR) and lower operational costs, as current methods rely heavily on trained personnel and resource-intensive call centers.
Innovation Solution
A computer method utilizing Adaptive Service Intelligence (ASI) data to perform root cause analysis by identifying impact and cause events, correlating them, and generating situation records, which includes a graphical representation of the relationship between these events to assist users in resolving network failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trained personnel and call centers are used to perform root cause analysis, then troubleshooting accuracy is improved, but operational costs and time consumption increase
Solution Approach 1:
The system enables automated self-diagnosis by having the network system itself generate and analyze logs, perform root cause analysis, and identify solutions without human intervention. The automated root cause analysis system processes logs, identifies events, determines causality relationships, and generates solution recommendations automatically, replacing the need for trained personnel to perform these tasks manually.
2Reliability
If trained personnel are deployed for troubleshooting, then problem resolution quality is improved, but operational costs increase
Solution Approach 1:
The patent replaces the mechanical system of human technicians performing manual analysis with an automated computational system. The automated root cause analysis system uses computer processors to execute algorithms that identify events, analyze log data, determine causality relationships, and generate solution recommendations, substituting human intellectual labor with automated information processing.
3Measurement precision
If manual triage and analysis processes are used, then accurate problem identification is achieved, but productivity decreases
Solution Approach 1:
The system performs preliminary automated analysis of log data and event identification before human intervention is needed. By pre-processing the logs, identifying events, and determining potential root causes automatically, the system prepares the groundwork for faster human decision-making or enables fully automated resolution, thereby increasing overall productivity while maintaining accuracy.
Data Source
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AI summary
A method for performing root cause analysis of failures in a computer network is provided. The method includes receiving an Adaptive Service Intelligence (ASI) data set related to one or more failures reported in the computer network from a plurality of interfaces. One or more impact events associated with the reported failures are identified based on the received ASI data set. Each of the identified impact events is correlated with one or more cause events. A situation record is selectively generated based on the correlation results.