QoS Improvement System for Transport Network Fault Detection
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Solution Overview
Problem
Telecommunication network service providers face difficulties in detecting and troubleshooting connectivity issues in carrier networks, as these networks operate as 'black-boxes' and vendors often disagree on performance data, hindering root cause analysis and optimal service delivery.
Innovation Solution
A Quality of Service (QoS) improvement system that collects performance metrics, applies clustering techniques to identify key performance indicators, and generates cluster maps to infer root causes of chronic performance issues, enabling efficient fault detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If carrier networks are used to provide backhaul support, then network connectivity is enabled, but fault detection and troubleshooting become difficult due to black-box operation
Solution Approach 1:
The patent introduces a QoS improvement system as an intermediary layer between the service provider and carrier networks. This system collects performance metrics from multiple sources including network elements, vendor data, and third-party sources, then analyzes this data to detect faults and provide root cause analysis. The intermediary system translates the black-box carrier network operations into visible, analyzable performance data.
Solution Approach 2:
The patent segments the fault detection process into multiple independent components: data collection from various sources, performance metric analysis, fault detection algorithms, and root cause analysis. This segmentation allows each component to be optimized independently and enables parallel processing of different network elements, improving overall detection capability while maintaining system manageability.
2Loss of information
If vendor performance data is used for troubleshooting, then some performance information is available, but data disagreement between vendors and service providers prevents accurate root cause analysis
Solution Approach 1:
The patent merges multiple data sources including vendor performance data, service provider network element data, and third-party performance metrics into a unified analysis framework. By combining these diverse data sources, the system creates a comprehensive view of network performance that can identify discrepancies and disagreements between different parties, leading to more accurate root cause analysis.
Solution Approach 2:
The system implements feedback mechanisms where performance metrics are continuously collected, analyzed, and used to generate actionable insights. The feedback loop includes comparing vendor data with independent measurements, identifying discrepancies, and using this information to improve future troubleshooting accuracy. The system also provides feedback to vendors about performance issues to encourage data alignment.
3Ease of operation
If manual troubleshooting methods are used with carrier vendors, then some issues can be identified, but the process is time-consuming and inefficient
Solution Approach 1:
The QoS improvement system enables self-service fault detection and diagnosis by automatically collecting performance metrics, analyzing data patterns, identifying faults, and providing root cause analysis without requiring extensive manual intervention. The system autonomously monitors network performance and generates troubleshooting recommendations, significantly reducing the time and effort needed compared to traditional manual methods.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and pre-analyzing performance metrics before faults occur. It maintains baseline performance data and detects deviations in real-time, allowing issues to be identified and addressed before they impact service quality. This proactive approach reduces troubleshooting time by having analysis results ready in advance.
4Quantity of substance
If no root cause analysis is available, then vendor performance data can be collected, but optimal service level cannot be achieved
Solution Approach 1:
The patent adds the dimension of root cause analysis to the existing performance data collection framework. Instead of merely collecting vendor performance metrics, the system analyzes this data across multiple dimensions including time patterns, network element relationships, and performance correlations to identify underlying causes of issues. This additional analytical dimension transforms raw data into actionable insights that enable optimal service delivery.
Data Source
AI summary
A Quality of Service (QoS) improvement system and method for transport network fault detection and QoS improvement so that a telecommunication network service provider can analyze the root cause on chronic performance issues and recommend potential solutions is disclosed. The system runs performance analysis on each AAV (mobile backhaul) or other transport networks and collects performance related metrics data. The system then selects a subset of the data related to certain key performance indicators (KPIs), such as latency, jitter, packet loss ratio, and availability. On this subset of KPI-related data, the system applies clustering techniques to identify clusters with similar performance issues. For each cluster, the system binds the AAV performance KPI data with one or more of the following site features—health, location, vendor, market, service type, etc.—to create a cluster map. The system can then generate inferences on root causes of the performance issues.