Network Analysis System for Service Degradation Root Cause Identification
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Solution Overview
Problem
Modern telecommunications networks face service degradation due to data traffic congestion, leading to data packet loss, queuing delays, and connection issues, which degrade Quality of Service (QoS) and Quality of User Experience (QoE) for end users.
Innovation Solution
A network-based approach that collects and correlates trace files from multiple nodes to identify the root cause of service degradation, providing alert notifications and optimization recommendations to network administrators, which can be implemented manually or automatically via preset configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data traffic demand increases to meet user needs, then service coverage and user access improve, but data traffic congestion occurs leading to packet loss and service degradation
Solution Approach 1:
The system performs preliminary actions by continuously collecting trace files from multiple nodes and analyzing them proactively to identify potential service degradation issues before they significantly impact users. This allows the network operator to take corrective actions in advance, preventing packet loss and service degradation that would otherwise occur during high traffic periods.
Solution Approach 2:
The system implements feedback by analyzing trace files from various network nodes and providing recommendations to the network operator. This feedback loop enables continuous optimization of network performance, allowing the system to adapt to increasing traffic demands while maintaining service quality through data-driven decision making.
2Reliability
If network monitoring and analysis capabilities are enhanced to identify service degradation causes, then service quality improves, but system complexity increases
Solution Approach 1:
The system segments the complex network monitoring task into manageable components by collecting trace files from individual nodes separately and then correlating them. This segmentation allows the system to handle complexity in a modular fashion, analyzing each node's data independently before integrating the results to identify service degradation causes.
Solution Approach 2:
The system introduces an intermediary analysis layer that correlates trace files from multiple nodes without requiring direct complex interactions between all nodes. This intermediary correlation process simplifies the overall system architecture by providing a structured method to integrate data from heterogeneous sources and generate actionable insights.
Data Source
AI summary
The techniques described herein involve analysis of client device Quality of Experience diagnostic files including an operations log or diagnostic files for a client device. The client device Quality of Experience diagnostic files may be generated by a client device and sent to a network node for analysis. The diagnostic files may be analyzed to determine device Key Performance Indicators and a device Quality of Experience, and to determine a root cause of a network problem (such as dropped calls) leading to a diminished Quality of Experience. In some embodiments, the diagnostic files may be aggregated to form a database of aggregated diagnostics, which can be used to further analyze a network to determine the root cause of a network problem. In some embodiments, the aggregated diagnostics may be indexed according to location, time, device type, device problem, or access technology.