QoE Optimization via Trace File Correlation
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Solution Overview
Problem
Modern telecommunications networks face service degradation due to data traffic congestion, leading to issues like data packet loss, queuing delay, and inability to establish connections, which degrade Quality of Service (QoS) and Quality of User Experience (QoE) for end users.
Innovation Solution
A 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 optimization, and automatically implements remedial actions to improve network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data traffic demand increases to serve more users, then network coverage and user access improve, but service degradation occurs due to congestion, packet loss, and queuing delay
Solution Approach 1:
The system performs preliminary actions by continuously collecting trace files from multiple network nodes and analyzing them to identify potential service degradation issues before they significantly impact users. The proactive nature of the system allows for early detection and remediation of problems such as packet loss and queuing delay, maintaining service quality even as network traffic increases
Solution Approach 2:
The system implements feedback mechanisms by monitoring network performance through trace file analysis and using this information to dynamically adjust network characteristics. The automated remedial actions modify network behavior based on observed conditions, creating a closed-loop control system that adapts to varying traffic loads while maintaining service quality
2Productivity
If network traffic volume increases, then more data services are provided, but Quality of Service (QoS) and Quality of User Experience (QoE) degrade
Solution Approach 1:
The system applies dynamics by automatically modifying network characteristics in response to changing traffic conditions. Rather than using static network parameters, the system dynamically adjusts network behavior based on real-time trace file analysis, allowing the network to adapt its performance characteristics to maintain QoS and QoE despite varying traffic volumes
Solution Approach 2:
The system changes network parameters automatically based on analyzed trace data. When service degradation is detected, the system modifies relevant network characteristics such as routing decisions, resource allocation, or traffic management parameters to restore optimal performance, enabling the network to maintain service quality across different traffic volumes
3Measurement precision
If manual analysis of network problems is performed, then root cause identification is possible, but response time is slow and service degradation persists
Solution Approach 1:
The system performs self-service by automatically collecting, analyzing, and responding to network performance issues without requiring manual human intervention. The automated trace file analysis and remedial action implementation enable the system to identify root causes and apply fixes much faster than manual processes, dramatically reducing both detection time and service degradation duration
Solution Approach 2:
The system replaces manual mechanical analysis processes with automated computational methods. Instead of human operators manually examining network data and determining root causes, the system uses automated trace file correlation and analysis algorithms that can rapidly process large volumes of network data and identify problems with both speed and precision
4Reliability
If network monitoring and analysis systems are implemented, then service degradation is detected, but system complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional monitoring and analysis platform that handles multiple network protocols, trace file formats, and analysis tasks through a unified architecture. This universal approach consolidates what could be multiple separate complex systems into a single integrated solution, reducing overall system complexity while maintaining comprehensive service degradation detection capabilities
Data Source
AI summary
The techniques described herein involve determining a context-based Quality of Experience based upon client device Quality of Experience diagnostic files in combination with client device equipment dynamics. Client device Quality of Experience (QoE) diagnostic files may indicate a reduced QoE at a client device, such as reduced signal strength or an increased number of dropped packets. User behavior during a reduced QoE event may be reflected as equipment dynamics, which may be included in equipment dynamics files. A service provider may receive information from the client device and may analyze the information to determine, with an increased confidence level, that the user device experiences a reduced QoE. Network resources may be allocated in response to the reduced QoE determination, thereby increasing a functioning of the computing network and an associated device's Quality of Experience.