Network Management System Detecting Wired Client Stuck
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Solution Overview
Problem
Existing network management systems fail to detect client devices that are not transmitting or receiving network data without error codes, leading to prolonged sub-par network performance and costly technician interventions.
Innovation Solution
A network management system that analyzes network statistics from ports of network devices to identify anomalies in client devices, using machine learning models to detect stuck clients and automatically perform remedial actions such as restarting or reconfiguring connected network devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network management systems rely on error codes from client devices to detect anomalies, then false positives are reduced, but detection capability is lost for devices that do not generate error codes
Solution Approach 1:
The patent introduces network devices (switches, routers) as intermediaries between client devices and the network management system. These intermediary devices collect network statistics (packet counts, bandwidth usage, connection states) that serve as indirect indicators of client device health, enabling detection of anomalies even when client devices do not generate error codes
Solution Approach 2:
The system continuously monitors network statistics from multiple sources (client devices, network devices) and uses this feedback to dynamically adjust anomaly detection thresholds and trigger remedial actions. The feedback loop enables the system to learn from historical data and improve detection accuracy over time
2Reliability
If network management systems implement comprehensive monitoring of all client devices, then anomaly detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The monitoring system is segmented into distributed components: network devices (switches, routers) that collect local statistics, a centralized network management system that aggregates data and performs analysis, and automated remediation modules. This segmentation allows comprehensive monitoring to be distributed across multiple simple components rather than requiring a single complex system
Solution Approach 2:
The system uses universal network statistics metrics (packet counts, bandwidth, connection states) that can be collected from multiple device types (switches, routers, access points) using standardized protocols. This multi-functionality allows the same monitoring approach to work across diverse network infrastructure without requiring device-specific complex logic
3Loss of time
If automated remedial actions are implemented for detected anomalies, then response time is reduced, but risk of incorrect automated actions increases
Solution Approach 1:
The system performs preliminary verification by collecting additional network statistics and comparing against historical baselines before executing automated remedial actions. Thresholds for triggering automated actions are set based on statistical significance, ensuring that only genuine anomalies trigger remediation. Common remedial actions (port shutdown, VLAN reconfiguration) are pre-configured and validated
Solution Approach 2:
After automated remedial actions are executed, the system continues to monitor the affected client devices to verify that the anomaly has been resolved. If the anomaly persists or worsens, the system can trigger alternative remedial actions or alert administrators. This feedback mechanism ensures that automated actions are effective and allows for corrective measures if the initial action was inappropriate
Data Source
AI summary
Techniques are described for detecting that a client device physically connected to a network device is “stuck,” that is, the client device is not sending or receiving network packets with the network device. A network management system (NMS) receives current network statistics of ports of network devices with respect to client devices physically connected to the ports. The NMS identifies a candidate client device connected to a particular port of a particular network device for which the current network statistics indicate an issue. The NMS detects anomalous behavior of the candidate client device based on one or more features of the current network statistics, historical baseline statistics associated with the candidate client device, and peer statistics associated with one or more peer client devices of a same device type as the candidate client device. The NMS outputs a notification of the anomalous behavior.