Digital Model Personas for Dynamic Network Testing
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Solution Overview
Problem
IT teams face challenges in efficiently troubleshooting and resolving network issues due to limited time and resources, as existing systems rely on static and monolithic test agents that fail to reflect current network activity and best practices.
Innovation Solution
The implementation of digital model personas that emulate human users, leveraging machine learning to replicate network behavior, allowing for dynamic testing and troubleshooting, and facilitating communication through human-readable platforms to diagnose and resolve issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static test agents are used for network testing, then device complexity is reduced, but adaptability to current network activity and best practices deteriorates
Solution Approach 1:
The patent implements dynamic test agents that can adapt their behavior based on current network conditions, user activity patterns, and performance metrics. These agents continuously learn from network data and modify their testing strategies accordingly, transforming static testing tools into dynamic, context-aware systems that evolve with the network environment.
Solution Approach 2:
The system changes multiple parameters of the test agents including their testing frequency, depth, scope, and methodology based on real-time network conditions, historical performance data, and detected anomalies. This allows the same agent infrastructure to operate in multiple modes from light monitoring to deep diagnostic analysis depending on what the network situation requires.
2Measurement precision
If manual troubleshooting by IT teams is performed, then measurement precision of network issues is improved, but loss of time increases
Solution Approach 1:
The patent implements self-service troubleshooting capabilities where the test agents autonomously diagnose and resolve common network issues without requiring human intervention. The system automatically analyzes test results, identifies root causes, and executes corrective actions, reserving human IT staff for complex problems that require creative problem-solving and judgment.
Solution Approach 2:
The system establishes continuous feedback loops where test results immediately inform subsequent testing and troubleshooting actions. The agents learn from each test outcome and adjust their diagnostic approach in real-time, rapidly narrowing down potential issues and accelerating the path to resolution while maintaining high diagnostic precision.
3Reliability
If comprehensive network monitoring is implemented, then reliability of network detection is improved, but use of energy and resources increases
Solution Approach 1:
The patent implements a tiered monitoring approach where the system performs comprehensive testing only when necessary, and uses lighter-weight monitoring for routine conditions. The test agents dynamically adjust the scope and intensity of their monitoring based on risk assessment, network criticality, and detected anomalies, performing excessive action only when the potential cost of missing an issue outweighs the resource consumption.
Solution Approach 2:
The monitoring system is segmented into multiple layers with different resource requirements. Critical network functions receive continuous high-fidelity monitoring, while less critical functions use periodic or event-driven monitoring. This segmentation allows the system to maintain high reliability for essential services while reducing overall resource consumption through differentiated monitoring strategies.
Data Source
AI summary
A network management station is configured to test a computer network through digital model personas. The network management station obtains network behavior data corresponding to user(s) of the computer network and generates digital model(s) based on the network behavior. The network management station deploys a first digital model persona based on a first digital model among the generated digital models. The first digital model persona operates at a first network location to test the computer network.