Autonomous Network Device Grouping for Self-Healing Management
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Solution Overview
Problem
Large area networks require efficient and autonomous management to reduce manual maintenance costs, especially in scenarios where direct human involvement is costly, necessitating a system that can autonomously discover and address malfunctioning or inefficient network devices without external intervention.
Innovation Solution
The system groups network devices based on attributes like type, functionality, and performance, using similarity metrics for self-monitoring and corrective actions, allowing devices to autonomously or semi-autonomously identify and address issues within the network, such as rerouting traffic or restarting devices, through consensus protocols and central server monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual maintenance and monitoring of network devices is performed, then reliability of network management is improved, but labor costs and operational complexity increase
Solution Approach 1:
The patent implements self-healing networks where network devices autonomously monitor their own status, detect failures, and execute corrective actions without human intervention. Each device maintains its own health metrics, compares performance against thresholds, and automatically triggers remediation protocols, enabling the system to serve and monitor itself.
Solution Approach 2:
The system continuously collects health metrics from network devices, compares actual performance against predefined thresholds, and automatically triggers corrective actions when deviations are detected. This closed-loop feedback mechanism enables autonomous decision-making and self-correction, maintaining network reliability through continuous monitoring and automated response.
2Productivity
If autonomous self-healing capabilities are implemented in network devices, then manual intervention costs are reduced, but device complexity and automation requirements increase
Solution Approach 1:
The patent divides autonomous maintenance functionality into distinct modular components: health metric collection modules, threshold comparison modules, and corrective action execution modules. Each network device contains these segmented functional units that operate independently but coordinate together, enabling autonomous operation through modular, manageable components rather than monolithic complexity.
Solution Approach 2:
The system implements universal health monitoring and self-healing capabilities that can be applied across diverse network device types. The same autonomous framework monitors various device attributes (CPU usage, memory, network traffic, error rates) and executes appropriate corrective actions regardless of device type, creating a multi-functional autonomous maintenance system.
3Loss of time
If network devices autonomously monitor and correct issues, then response time to failures is improved, but system complexity and coordination requirements increase
Solution Approach 1:
The patent pre-configures threshold values for health metrics and predefined corrective actions for various failure conditions. When a device monitors its health metrics, it immediately compares against pre-established thresholds and executes pre-programmed remediation protocols. This preliminary preparation enables instantaneous autonomous response to failures without complex real-time decision-making or coordination delays.
Data Source
AI summary
A method includes forming a logical group of network devices from a plurality of network devices based on at least one attribute of the network devices. The method further includes selecting at least one similarity metric for the logical group of network devices. The method also includes determining a value of the similarity metric for each of the network devices of the logical group. The method further includes comparing values of the similarity metric corresponding to each of the network devices of the logical group against a threshold value. The method also includes determining an action to be taken at one or more of the network devices based on the comparison between the values of the similarity metric and the threshold. There may also be multiple interconnected groups, each performing these actions independently and conveying the computed information between each other.