AI SDN Controller Fault Management for Network Equipment
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Solution Overview
Problem
Current network fault management in SDN OpenFlow protocol systems relies on human intervention, which is time-consuming and prone to errors, leading to prolonged downtime and network disruptions.
Innovation Solution
A machine learning technology-based fault management system utilizing an AI-based SDN controller and a Knowledge-converged Super Brain AI framework to automatically detect and respond to network faults by analyzing past data and providing scenario-based commands to network equipment via the OpenFlow protocol.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human specialists manually detect and manage network faults, then fault management can be performed with human judgment and experience, but it takes tens of minutes to several hours and is prone to human error
Solution Approach 1:
The system enables self-service by implementing an automated fault management system where the AI-based SDN controller automatically detects faults through SNMP agents, analyzes them using machine learning algorithms, and executes recovery commands without human intervention. The system learns from historical fault data and autonomously performs fault management tasks that previously required human specialists.
Solution Approach 2:
The patent replaces the mechanical human judgment and manual operations with an AI-based automated system. Machine learning algorithms substitute human specialists' decision-making processes, while automated command execution replaces manual configuration changes. This substitution eliminates human error and dramatically reduces fault management time from hours to minutes or seconds.
2Ease of operation
If human specialists manage network faults, then flexible judgment can be applied based on technical experience, but disagreements among staff prolong problem resolution and human errors can affect the entire network
Solution Approach 1:
The AI-based SDN controller serves multiple functions: it detects faults through SNMP agents, analyzes fault types using machine learning, determines appropriate recovery scenarios, and executes commands across diverse network equipment. This universal system replaces multiple human specialists with a single automated entity that consistently applies learned knowledge from historical data, eliminating disagreements while maintaining operational flexibility.
Solution Approach 2:
The system implements feedback by continuously learning from historical fault management data and outcomes. The machine learning algorithms analyze past fault patterns, recovery actions, and results to improve future fault detection and resolution. This feedback loop ensures the system adapts and improves over time, providing consistent, reliable decisions based on accumulated knowledge rather than variable human judgment.
Data Source
AI summary
A machine learning technology-based fault management system for network equipment that supports SDN OpenFlow protocol that includes an L2 switch or a router, which is network equipment connected to a client; and an Artificial Intelligence (AI)-based Software Defined Network (SDN) controller requested for management commands for each scenario when the L2 switch or the router, which is network equipment connected to the client, encounters a network fault so that a Simple Network Management System (SNMP) agent installed in the L2 switch and the router determines the type of fault occurred on a network and AI is employed to recover from a current fault through learning results from past data. An effect is achieved that not only service quality is improved through real-time fault management using an AI-based automatic response against a network fault but also a fault is precisely overcome by using the AI-based automatic response.