Network Incident Prediction Using ML Anomaly Detection
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Solution Overview
Problem
Computer networks experience errors and outages due to hardware and software components, requiring manual administrator intervention for restoration, which is inefficient and can lead to costly consequences like revenue loss and negative customer experiences.
Innovation Solution
A system for network incident management that aggregates network metrics, generates a training data set using machine learning algorithms to predict incidents, and provides real-time alerts and preventive actions through a user interface, utilizing a historical network data database and control circuit to automate the detection and response to potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual administrator intervention is used to identify and address network issues, then the system complexity remains low, but the response time is slow and productivity is reduced
Solution Approach 1:
The system enables automatic self-diagnosis and self-reporting of network incidents through machine learning models that continuously monitor system metrics and autonomously identify anomalies, eliminating the need for manual administrator intervention in incident detection and initial response
Solution Approach 2:
Manual mechanical processes of administrator monitoring and diagnosis are replaced with automated electronic systems including machine learning incident models that process system metrics and generate incident reports automatically, significantly improving response speed
2Reliability
If manual monitoring and response methods are used, then the device complexity is low, but the reliability of network operation deteriorates due to longer downtime
Solution Approach 1:
The system performs preliminary detection and classification of potential incidents before they cause actual network failures, allowing administrators to take preventive actions in advance, thus improving network reliability by preventing outages before they occur
Solution Approach 2:
The system continuously collects system metrics, compares them against learned patterns, and provides real-time feedback about incident likelihood and system health, enabling continuous improvement of network reliability through automated monitoring and alerting
3Loss of time
If traditional manual incident management is used, then the ease of operation is maintained, but the loss of time increases due to manual identification and response delays
Solution Approach 1:
The incident management system performs automatic self-monitoring, self-diagnosis, and self-reporting of incidents, eliminating manual intervention for incident identification and reducing response time while maintaining operational simplicity through automated workflows
Data Source
AI summary
Systems, apparatuses, and methods are provided herein for network incident management. A method for network incident management comprises aggregating network metrics associated with a monitored network in a historical network data database, identifying incidents based on the network metrics, generating a training data set based on the network metrics and the incidents, wherein the training data set comprises time series of network metrics as training input and incidents as labels, training an incident model using the training data set, receiving real-time network metrics from the network via the network interface, determining an incident prediction based on the incident model using the real-time network metrics as input, and causing a user interface device to provide an alert to a user based on the incident prediction.