ML-Based QoS Value Correction for Network Traffic Monitoring
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Solution Overview
Problem
Traditional communication networks face challenges in accurately monitoring and managing data flow behaviors across multiple devices, leading to misidentification of Quality of Service (QoS) values, which can result in incorrect issue diagnosis and mismanaged network resources.
Innovation Solution
A trained machine learning model is used to monitor data flow behaviors, label data packets based on their performance, and update QoS values, enabling real-time identification of issues and accurate troubleshooting by correlating packet behaviors with stored labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional monitoring methods are used to track data flow across multiple devices, then device compatibility and network coverage are improved, but measurement precision of QoS values deteriorates
Solution Approach 1:
A machine learning model is introduced as an intermediary between raw data flow monitoring and QoS value determination. The model processes data flow behaviors from multiple devices and outputs corrected QoS values, acting as a mediator that reconciles the conflict between monitoring diverse devices and maintaining measurement accuracy.
Solution Approach 2:
Traditional rule-based QoS determination mechanisms are replaced with a machine learning-based system. The ML model learns patterns from data flow behaviors and automatically determines QoS values without relying on rigid mechanical rules, enabling both broad device compatibility and high measurement precision.
2Measurement precision
If machine learning models are used to determine QoS values, then measurement precision and issue detection accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
The machine learning model performs self-training and self-adjustment by learning from data flow behaviors autonomously. The system automatically updates its understanding of QoS relationships without requiring manual configuration or complex external management, reducing the operational complexity despite the sophisticated underlying model.
3Reliability
If real-time data flow monitoring is implemented across all devices, then reliability of issue detection is improved, but loss of time for data processing increases
Solution Approach 1:
The machine learning model is trained in advance on historical data flow behaviors and QoS values. This preliminary training enables the model to quickly process real-time data without requiring complex analysis during actual operation, reducing processing time while maintaining high detection reliability.
Data Source
AI summary
Systems and methods are provided for monitoring traffic flow using a trained machine learning (ML) model. For example, in order to maintain a stable level of connectivity and network experience for the devices in a network, the ML model can monitor the data flow of each device and label each data flow based on its behavior and properties. The system can take various actions based on the labeled data flow, including generate an alert, automatically change network settings, or otherwise adjust the data flow from the device.