Network Element Risk Prediction via ML Composite Quality Index
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Solution Overview
Problem
Managing and predicting risks in large and complex modern networks is challenging due to the complexity of network architectures and the lack of proactive tools to identify high-risk elements that can impact customer experience, leading to increased operational expenses and manual troubleshooting difficulties.
Innovation Solution
A method and system using a composite quality index with weighted components, historical baseline values, and a trained machine learning model to compute risk scores and predict future risks in network elements, enabling proactive identification of potential service degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual network management methods are used, then operational flexibility is maintained, but operational expenses increase and service quality deteriorates as network size grows
Solution Approach 1:
The system enables network elements to self-diagnose and self-report their status by automatically collecting performance metrics, computing quality indices, and identifying anomalies without human intervention. This self-service capability allows the network to manage itself, reducing operational expenses while maintaining service quality despite growing network complexity
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with automated computational systems that use machine learning models, statistical analysis, and algorithmic anomaly detection. This substitution eliminates the need for human operators to manually analyze each network element, enabling efficient management of large-scale networks
2Reliability
If reactive tools are used to process network measurements, then failure analysis can be performed after events occur, but proactive risk prediction capability is lacking
Solution Approach 1:
The system performs preliminary actions by continuously monitoring network elements and computing quality indices to identify anomalies before they cause service failures. The machine learning models predict potential failures in advance, allowing operators to take preventive measures before actual service degradation occurs, thus improving reliability while reducing response time
Solution Approach 2:
The system implements continuous feedback loops where performance metrics are collected, analyzed, and used to update risk predictions and trigger alerts. This feedback mechanism enables the system to learn from historical data and improve its predictive accuracy over time, maintaining high service quality through proactive intervention
3Productivity
If the number of network elements increases, then network capacity and coverage improve, but the number of potential failure points increases proportionally
Solution Approach 1:
The system segments the large network into individual network elements, each independently monitored and evaluated. By computing quality indices for each element separately and identifying anomalies at the element level, the system can manage risk in large networks without being overwhelmed by the sheer number of components, maintaining network capacity while controlling failure risk
Data Source
AI summary
A method, system and computer-readable medium where a weighted composite quality index having a plurality of components for a network element is identified. A historical baseline value from historical data for each component is determined, and a deviation from the historical baseline values is measured. A risk level for the deviation is assigned. A loss score for the measured components is computed by mapping the risk level to a numerical score. An aggregated risk score based on a sum of weighted risk scores for each of the components is computed. An expected risk score based on probabilities associated with the aggregated risk score is determined by computing future probabilities of each risk level at the network element based on a trained machine learning model.