QoE Optimization System for Network Congestion Analysis
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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 QoE optimization system that collects and correlates trace files from multiple nodes in the network to identify the root cause of service degradation, providing alert notifications and recommendations for remedial actions to network administrators, or automatically implementing optimizations when service levels are not met.
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 expand, but data traffic congestion occurs leading to packet loss and queuing delays
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network conditions and predicting potential congestion points before they occur. Trace files are collected and analyzed in advance to identify degradation patterns, allowing the system to proactively implement remedial actions such as load balancing or resource allocation adjustments before packet loss and queuing delays occur, thus maintaining both service coverage and transmission reliability
Solution Approach 2:
The system establishes a feedback loop by continuously collecting trace files from network nodes, analyzing QoS metrics, comparing actual performance against service level agreements, and automatically implementing optimizations when degradation is detected. This closed-loop feedback mechanism enables the system to adapt to changing traffic conditions in real-time, ensuring reliable data transmission while maintaining expanded service coverage
2Measurement precision
If network monitoring and analysis capabilities are enhanced to identify root causes, then QoE optimization improves, but system complexity increases
Solution Approach 1:
The system segments the complex network monitoring task into distinct functional modules: trace file collection from multiple nodes, correlation of trace data across nodes, analysis of QoS metrics, comparison against service level agreements, and automated optimization implementation. This segmentation allows each module to specialize in a specific function, improving measurement precision for service degradation detection while managing system complexity through modular architecture
Solution Approach 2:
The system introduces an intermediary QoE optimization system that acts as a mediator between raw network data and complex analysis requirements. This intermediary layer collects trace files from multiple network nodes, correlates the data, and presents processed information for analysis, thereby enhancing detection accuracy without requiring direct complex interactions between all system components
Data Source
AI summary
The techniques described herein involve analysis of communication data included in trace file(s) of device(s) involved in a communication. These trace file(s) may each include data associated with multiple layers of a communication protocol stack of a respective device or data associated with a single such layer. The techniques may further involve one or more of determination of performance metrics associated with data at a specific layer of a specific device, correlation of the data between layers of a device, or correlation of data across multiple device(s) involved in the communication. The performance metrics or correlated data may then be analyzed based on thresholds or models to determine whether the performance metrics or correlated data exhibits a degraded quality of user experience. Also or instead, graphic or textual representations of the performance metrics or correlated data may be generated.