Multi-Domain Network Root Cause Identification via Layered Visualization
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Solution Overview
Problem
Existing multi-domain network monitoring technologies fail to isolate and identify the exact root cause of network performance degradation across interconnected domains, leading to challenges in diagnosing and resolving outages effectively.
Innovation Solution
A system utilizing real-time data collection, correlation, and layered visualization across multiple domains, incorporating performance data, alarm data, and configuration logs to categorize and map network elements, thereby identifying the exact root cause of network issues and reducing Mean Time to Repair (MTTR).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network monitoring is used, then network performance can be monitored, but the exact root cause of network issues cannot be identified
Solution Approach 1:
The monitoring system segments the network into multiple domains (core network, radio access network, transmission network) and further divides each domain into functional layers. This segmentation allows the system to monitor and analyze each layer independently while maintaining overall network context, enabling precise root cause identification without overwhelming complexity.
Solution Approach 2:
The system introduces a layered dimension to traditional network monitoring by organizing network elements across multiple layers (Layer 1-7 OSI model). This dimensional transformation allows monitoring data to be structured and analyzed from multiple perspectives simultaneously, making root cause identification more precise while managing complexity through structured organization.
2Loss of information
If comprehensive network monitoring across all domains is implemented, then complete visibility is achieved, but analysis time and complexity increase
Solution Approach 1:
The system segments monitoring data into domain-specific datasets (core network, radio access network, transmission network) with associated KPIs and alarm data. This segmentation enables parallel processing and targeted analysis of specific domains when issues arise, reducing overall analysis time while maintaining comprehensive visibility through the structured organization of data across segments.
Solution Approach 2:
The system performs preliminary organization and categorization of monitoring data into standardized layers and domains before actual analysis is needed. By pre-structuring the data architecture and establishing correlation relationships between layers in advance, the system enables rapid analysis during incidents without time-consuming data organization, thus reducing analysis time while maintaining complete visibility.
3Measurement precision
If multi-domain network elements are monitored individually, then detailed monitoring is achieved, but correlation between domains is lost
Solution Approach 1:
The system merges monitoring data from multiple domains (core network, radio access network, transmission network) while preserving the detailed structure of each domain. By combining data across domains through standardized layer-based frameworks and correlation mechanisms, the system maintains both detailed monitoring of individual elements and inter-domain correlation information simultaneously.
Solution Approach 2:
The system implements a universal layered monitoring framework that can accommodate and monitor network elements across all domains and layers (Layer 1-7). This universal structure enables detailed monitoring of specific elements while simultaneously establishing correlations between domains through the common layer-based reference, thus preserving both monitoring detail and inter-domain relationships.
4Speed
If real-time monitoring of all network elements is implemented, then immediate detection is achieved, but system resource consumption increases
Solution Approach 1:
The system segments real-time monitoring into domain-specific and layer-specific monitoring tasks, allowing parallel processing of monitoring data across different networks. This segmentation enables immediate detection of issues in any domain while managing system resources by focusing real-time analysis on the most critical layers and domains based on current network state and priority.
Solution Approach 2:
The system dynamically adjusts monitoring intensity and resource allocation based on current network conditions, alarm levels, and identified issues. By making monitoring resources dynamic rather than static, the system maintains real-time detection capability for critical issues while reducing resource consumption during normal operation through adaptive monitoring strategies that prioritize based on network state.
Data Source
AI summary
The present invention relates to a system and method for monitoring multi-domain network using end-to-end layered visualization to identify exact root cause of network element to prevent degradation in the network performance is disclosed. The system comprises of data collection module, correlation module, mapping module and management server. The data collection module collects performance data, alarm data, configuration logs and signalling traces from one or more network elements through the management server to draw connectivity across one or more layers. The correlation module correlates the collected performance data, alarm data and configuration logs at regular intervals in order to isolate the root cause of the issue identified. This correlated data of all the network elements are superimposed with layered visualization and mapped by the mapping module and thereby identify the exact root cause of network element causing degradation in the network performance.