Network Root Cause Prediction via Historical Behavior Comparison
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Solution Overview
Problem
Identifying the root cause of network issues in data centers is challenging due to the complexity of network components and the similarity of symptoms across different issues, leading to inefficient troubleshooting and resolution processes.
Innovation Solution
A method that predicts the root cause of current network issues by comparing the behavior of network components during the current issue with previous issues, using user input from past diagnoses to identify patterns and similarities, and storing this information in a database for future predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems collect and analyze network data to detect issues, then issue detection capability is improved, but the time to identify root cause increases due to manual troubleshooting complexity
Solution Approach 1:
The system performs preliminary actions by collecting and storing network component behavior data during issues before the current issue occurs. Historical data about component behavior patterns is pre-acquired and stored in the database, enabling rapid comparison when a new issue arises without requiring manual analysis of past issues during troubleshooting.
Solution Approach 2:
The system creates a copy of historical network issue patterns and component behavior data stored in the database. When a new issue occurs, the system compares current component behavior against these copied historical patterns to automatically identify similar issues and their root causes, eliminating the need for manual analysis of past issues.
2Measurement precision
If network administrators manually troubleshoot each issue individually, then troubleshooting accuracy is maintained, but productivity decreases due to repetitive manual analysis
Solution Approach 1:
The system enables self-service by automatically comparing current network component behavior against historical data to identify root causes without requiring administrator intervention in the analysis process. The system serves itself by autonomously performing pattern matching and root cause identification, allowing administrators to simply initiate the analysis and receive results.
Solution Approach 2:
The system implements feedback by using historical issue resolution data to automatically determine root causes for current issues. The feedback loop involves comparing current component behavior patterns against stored historical patterns, identifying similar issues, and automatically determining root causes based on the feedback from historical data, thereby maintaining accuracy while improving speed.
3Measurement precision
If the system stores and analyzes historical network issue data, then root cause prediction accuracy improves, but device complexity increases due to data management requirements
Solution Approach 1:
The system extracts only the necessary behavior data and key patterns from historical network issues and stores them in the database. Rather than storing complete issue records, the system extracts and stores only the essential component behavior patterns and root cause information, reducing data management complexity while maintaining prediction accuracy.
Solution Approach 2:
The system segments the data management task by organizing historical data into structured categories (component behavior patterns, issue types, root causes) and storing them separately in the database. This segmentation allows the system to manage large amounts of historical data efficiently through structured storage and retrieval mechanisms, reducing overall complexity.
Data Source
AI summary
Determining root causes of network issues includes identifying a previous network issue, comparing a first behavior of network components during the previous network issue with a second behavior of the network components during a current issue of a network, and obtaining a previous user input about a previous root cause of the previous network issue collected while diagnosing the previous issue.