Predicting Network Control Plane Instabilities
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Solution Overview
Problem
Control plane instabilities in computer networks, such as 'flapping' where routers rapidly switch between different paths, are difficult to predict and manage, often resulting from misconfigurations, environmental changes, and other conditions, leading to complex troubleshooting and unstable network configurations.
Innovation Solution
A device in the network uses a machine learning model to predict control plane instabilities by analyzing control plane packet data, allowing for proactive mitigation actions to prevent instability before it occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control plane traffic is exchanged for path identification and signaling, then network devices can identify other devices and discern best network paths, but control plane instabilities such as flapping occur causing rapid path changes and network instability
Solution Approach 1:
The patent applies preliminary action by using a machine learning model to predict control plane instabilities before they occur. The system analyzes historical control plane packet data to identify patterns and predict future instabilities, allowing network operators to take preventive measures before actual flapping events happen, thus maintaining control plane stability while enabling necessary control plane traffic exchange
2Reliability
If machine learning models are used to predict control plane instabilities, then proactive mitigation actions can be taken to prevent instability, but the complexity of the network system increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that sits between the control plane packet data and the stability prediction outcome. This intermediary component processes raw control plane traffic data, extracts relevant features, and generates stability predictions without requiring direct complex interactions between all system components, thus managing system complexity while enabling reliable instability prediction
Data Source
AI summary
In one embodiment, a device in a network receives control plane packet data indicative of control plane packets for a control plane in the network. The device models the control plane using a machine learning model based on the control plane packet data. The device predicts an instability in the control plane using the machine learning model. The device causes performance of a mitigation action based on the predicted instability in the control plane.