Alarm Correlation Engine for Root Cause Detection
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Solution Overview
Problem
Alarm overload in complex communications networks makes it difficult for administrators to timely identify the root cause of incidents, leading to potential network degradation and increased operator time and expense.
Innovation Solution
An alarm correlation engine analyzes description data from multiple network nodes to determine a subset of alarms representing a network incident, applies this subset to a machine learning model to predict the likelihood of root causes, and updates the model based on actual root cause determinations for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional alarm monitoring methods are used in complex communication networks, then complete alarm data is collected from all nodes, but alarm overload occurs and administrators cannot timely identify root causes
Solution Approach 1:
The alarm management system segments the overwhelming set of all alarms into meaningful groups by identifying alarm correlations and hierarchies. The system divides alarms into root cause alarms and symptom alarms through correlation analysis, allowing administrators to focus on a segmented, manageable subset rather than reviewing every alarm individually. This segmentation resolves the contradiction by maintaining complete data collection while enabling timely root cause identification through structured organization.
Solution Approach 2:
The patent introduces an alarm correlation engine as an intermediary between alarm generation and administrator analysis. This intermediary component automatically processes raw alarm data, establishes correlations between alarms, determines hierarchies, and presents processed information to administrators. The correlation engine acts as a mediator that filters and structures alarm information, resolving the time loss contradiction by performing preliminary analysis work that would otherwise require administrator time.
2Measurement precision
If administrators manually analyze all network alarms to identify root causes, then comprehensive analysis is performed, but operator time and expense increase significantly
Solution Approach 1:
The alarm management system implements self-service by automatically performing correlation analysis, hierarchy determination, and root cause identification without requiring manual administrator intervention for each alarm. The system serves itself by maintaining and updating alarm correlation relationships automatically, using machine learning models to predict root causes, and presenting pre-analyzed information to administrators. This self-service capability maintains high identification accuracy while eliminating the need for extensive manual operator time and expense.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with automated computational systems. Machine learning models substitute for human administrators in performing correlation analysis and root cause prediction. The system uses automated algorithms to process alarm data, establish relationships, and identify root causes, replacing the mechanical effort of manual analysis with efficient computational processes that maintain accuracy while reducing operator time and expense.
3Loss of information
If alarm correlation analysis is performed on all alarms from all nodes, then complete incident understanding is achieved, but system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary action by pre-establishing alarm correlation relationships and hierarchies before incidents occur. The alarm correlation engine continuously analyzes alarm data to build and maintain correlation models in advance, so that when incidents occur, the pre-computed relationships can be quickly applied without requiring complex real-time analysis of all alarms. This preliminary preparation maintains information completeness while reducing the complexity of incident-time processing.
Solution Approach 2:
The patent applies parameter changes by transforming raw alarm data into structured correlation relationships with defined hierarchies and weights. The system changes the parameters of alarm representation from simple individual alarm events to multi-dimensional correlation structures that include relationship strength, hierarchy levels, and predictive probabilities. This parameter transformation maintains complete incident information while organizing it in a less complex, more manageable format that facilitates quicker analysis.
Data Source
AI summary
In various examples, description data may be used by an engine to correlate a subset of alarms representing a network incident. A machine learning model may then be used to predict a likelihood that one or more of the alarms within the subset is a root cause of the network incident. This root cause may then be displayed on a graphical user interface. As a result, alarm fatigue experienced by network administrators may be reduced.