ML-Based Network Device Utilization Analysis
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Solution Overview
Problem
Current methods lack the ability to effectively monitor and understand the confluence of configuration and load conditions affecting network device performance, leading to issues like high packet loss and latency, and fail to provide clear recommendations for improving network conditions, often resulting in increased downtime and inefficient troubleshooting.
Innovation Solution
A method utilizing a trained machine learning model to analyze network device performance data, identifying key variables affecting performance, and generating a user interface to display visual representations of the analysis, which includes root cause analysis and pre-change impact predictions to recommend corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network monitoring methods are used, then basic device status can be tracked, but the confluence of configuration and load conditions cannot be effectively analyzed
Solution Approach 1:
A machine learning model serves as an intermediary between raw network device data and performance analysis. The model processes configuration and load condition data to identify root causes of performance issues, enabling precise performance analysis without requiring complex manual analysis systems.
Solution Approach 2:
Traditional manual or basic automated monitoring methods are replaced with machine learning-based analysis. The ML model automatically processes multiple variables (configuration and load conditions) to identify performance issues, substituting complex manual analysis with an intelligent system.
2Reliability
If comprehensive monitoring of configuration and load conditions is implemented, then root cause analysis becomes possible, but system complexity increases
Solution Approach 1:
The machine learning model performs self-service by automatically analyzing configuration and load condition data to identify root causes of network performance issues. This eliminates the need for complex manual troubleshooting procedures while maintaining comprehensive monitoring capabilities.
Solution Approach 2:
The system monitors changes in multiple parameters (configuration settings and load conditions) simultaneously. By analyzing how these parameters change over time and their relationships, the system identifies root causes without requiring overly complex monitoring infrastructure.
3Loss of time
If real-time network performance monitoring is implemented, then downtime can be reduced, but resource consumption increases
Solution Approach 1:
The machine learning model performs preliminary analysis of network device data to predict and identify performance issues before they cause significant downtime. By analyzing configuration and load conditions proactively, the system can alert administrators in advance, reducing actual network downtime while optimizing resource usage through targeted monitoring.
Data Source
AI summary
In one aspect, a method of utilization analysis of network devices include receiving a set of information associated with performance of the network devices operating in a network, processing using a trained machine learning model the set of information to identify one or more variables indicative of performance of the network devices, the machine learning model being trained to receive as input the set of device utilization information, identify the one or more variables, and provide as output an analysis of the performance of one or more of the network devices, and generating a user interface to display on a user device a visual representation of the output of the trained machine learning model.