Network Event Cause Identification Using Element Influence Mapping
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Solution Overview
Problem
Existing communication systems struggle to accurately identify the root cause of events, leading to ineffective auto-healing and potential multi-failures, especially when the event originates in a core network system affecting a radio access network.
Innovation Solution
A cause identifying system and method that utilizes inventory data to determine the influence relationship between elements in a communication system, identifying the cause of an event based on geographical or topological closeness and executing appropriate actions, such as healing, on the identified element.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automatic healing is executed on an element when an event occurs, then the response time is reduced, but the accuracy of identifying the root cause deteriorates leading to potential multi-failures
Solution Approach 1:
The system performs preliminary analysis by identifying influence relationships between elements before executing healing operations. The cause identification unit analyzes the current status of elements and their influence relationships to determine the actual root cause, ensuring accurate targeting before any healing action is taken.
Solution Approach 2:
The system continuously monitors the status of elements and uses this feedback to refine the cause identification. By analyzing the current status of elements and their influence relationships in real-time, the system can accurately identify the root cause and adjust healing operations accordingly, preventing multi-failures.
2Ease of repair
If healing operations are performed on the radio access network when a failure occurs, then the local problem is addressed, but the overall system reliability deteriorates due to potential multi-failures from incorrect targeting
Solution Approach 1:
The cause identification unit acts as an intermediary between the failure detection and the healing execution. It analyzes the influence relationships and current status of elements to determine the actual root cause, ensuring that healing operations are targeted correctly and do not cause multi-failures in the broader system.
Solution Approach 2:
Before executing any healing operation, the system performs a preliminary analysis of the influence relationships and current status of elements. This ensures that the healing action is directed at the correct root cause rather than just the symptomatic element, maintaining system reliability while resolving local problems.
3Measurement precision
If manual analysis of event causes is performed, then the accuracy of root cause identification is improved, but the operational time and labor increase significantly
Solution Approach 1:
The system performs self-diagnosis by automatically analyzing the current status of elements and their influence relationships to identify the root cause. The cause identification unit autonomously determines the affected element without requiring manual intervention, thereby maintaining high accuracy while significantly reducing operational time and labor.
Solution Approach 2:
The system replaces manual analysis mechanisms with automated computational analysis. By using the cause identification unit to process status information and influence relationships algorithmically, the system achieves accurate root cause identification without the time and labor requirements of manual investigation.
4Device complexity
If the influence relationship between elements is not considered, then the system complexity is reduced, but the ability to accurately identify root causes deteriorates
Solution Approach 1:
The cause identification unit serves multiple functions: it analyzes the current status of elements, determines influence relationships, and identifies the root cause. By consolidating these functions into a single integrated unit, the system achieves accurate root cause identification without proportionally increasing overall system complexity.
Data Source
AI summary
An inventory database and an active inventory each store inventory data indicating a current status of an influence relationship between elements included in the communication system. An E2EO module detects occurrence of an event in a specific element included in the communication system. The E2EO module identifies at least one other element having an influence relationship with the specific element based on the inventory data. The E2EO module identifies a cause of the event based on a status of the at least one other element.


