Network Configuration Failure Prediction for Access Control Servers
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Solution Overview
Problem
Managing networks with diverse devices having different network-related attributes is challenging, leading to potential network configuration failures and disruptions.
Innovation Solution
A network access control and management server employs a machine-learning based failure prediction model to identify and proactively address network configuration issues, using predictive analytics to preemptively trigger corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual network configuration management is used for diverse devices, then network stability can be maintained through careful monitoring, but the complexity of managing different network-related attributes increases significantly
Solution Approach 1:
The system enables self-service through automated failure prediction and corrective action execution. The machine learning model continuously monitors network devices and automatically predicts configuration failures before they occur, allowing the system to self-correct without manual intervention. This reduces the complexity burden on administrators while maintaining network stability.
Solution Approach 2:
The patent applies preliminary action by predicting network configuration failures before they actually occur. The machine learning model analyzes current device states and historical data to forecast potential failures, enabling administrators to take preventive measures ahead of time. This proactive approach simplifies management by addressing issues before they disrupt network stability.
2Reliability
If proactive failure prediction is implemented using machine learning, then network configuration failures can be prevented, but the computational resources and processing time required increase
Solution Approach 1:
The system applies partial action by focusing computational resources on predicting only the most critical configuration failures rather than analyzing every possible failure mode. The machine learning model prioritizes predictions based on risk assessment, allocating computational energy efficiently to high-impact scenarios while maintaining effective failure prevention.
3Productivity
If automated corrective actions are triggered preemptively, then network disruptions are minimized, but the risk of incorrect automated interventions increases
Solution Approach 1:
The system implements feedback mechanisms where the results of automated corrective actions are continuously monitored and fed back into the machine learning model. This feedback loop allows the system to learn from past interventions, improving the accuracy of future predictions and reducing the risk of incorrect automated actions. The model adjusts its predictions based on the actual outcomes of previous corrective measures.
Data Source
AI summary
A method of operating a server is provided that includes providing, with the server, one or more services relating to network access control and management of a network, predicting a network configuration failure associated with the network with a failure prediction model, and generating a network configuration recommendation based on the predicted network configuration failure to avoid the predicted network configuration failure. The failure prediction model can be a machine-learning based network configuration failure prediction model that is trained on past network configuration failure events. Operated in this way, erroneous network configuration issues can be automatically identified and addressed in a timely fashion.