AI Server Management System for Proactive Fault Prediction
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Solution Overview
Problem
Managing large numbers of servers is challenging due to the scarcity of specialized personnel, leading to difficulties in responding promptly to server faults, which can result in significant losses, especially in small companies where professionals are not readily available or are remote.
Innovation Solution
A server management system utilizing AI technology to collect and analyze data from multiple servers, predict potential faults, and send alerts to administrators and customers, thereby enabling proactive fault prevention and reducing operational costs and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a server administrator with specialized skills is hired, then the server management quality improves, but the hiring cost increases significantly
Solution Approach 1:
The server management system enables automated self-monitoring and self-diagnosis of server faults. The system collects server data, analyzes it using AI algorithms, and automatically generates fault predictions and notifications without requiring constant human intervention, allowing the system to serve itself in terms of basic monitoring and alerting functions
Solution Approach 2:
The patent replaces the mechanical system of human administrators manually monitoring and diagnosing server issues with an automated electronic system using AI and machine learning algorithms. The system substitutes human expertise with computational models that can analyze server data, predict faults, and notify administrators automatically
2Quantity of substance
If an existing personnel is appointed as server administrator instead of hiring a professional, then the hiring cost reduces, but the server management capability and fault response efficiency deteriorate
Solution Approach 1:
The system performs automated data collection, analysis, and fault prediction without requiring specialized administrative skills. The AI-driven system handles the complex technical analysis that would otherwise require professional knowledge, enabling any personnel to effectively manage server monitoring through the automated interface
Solution Approach 2:
The patent introduces an intermediary AI system between the server infrastructure and the administrator. This intermediary handles the complex technical analysis and translates it into actionable notifications, bridging the gap between technical server operations and non-expert administrators
3Adaptability or versatility
If the server administrator is remote from the server, then the operational flexibility improves, but the fault response time increases significantly
Solution Approach 1:
The system implements automated feedback loops where server data is continuously collected, analyzed, and used to generate real-time notifications. When faults are detected or predicted, the system immediately feeds back alerts to administrators through multiple channels (SMS, email, push notifications), eliminating the delay associated with remote monitoring
Solution Approach 2:
The AI system performs preliminary analysis and fault prediction before actual failures occur. By analyzing patterns and trends in server data, the system can predict potential issues and notify administrators in advance, allowing them to take preventive action before faults impact server operations
4Reliability
If AI technology is used to predict server faults, then the fault prevention capability improves, but the system complexity increases
Solution Approach 1:
The patent segments the complex AI system into distinct functional modules: data collection module, data preprocessing module, AI analysis module, and notification module. Each module handles a specific aspect of the fault prediction process, making the overall system more manageable and easier to implement despite the advanced capabilities
Data Source
AI summary
Provided is a server management system managing two or more management target servers, including: a database for storing data related to the management target servers; and a management server collecting hardware-related data and software-related data from the management target servers, identifying and managing a status of each management target server, and providing various server management information including management service statistical data and a management service report to an administrator terminal used by an administrator and a customer terminal requesting the management target server, wherein the management server analyzes the management target server by using AI technology, predicts an status and a fault of the management target through this analysis, and through the prediction, when an issue occurs, transfers an alarm message as a text message to the relevant administrator terminal and customer terminal. There is an effect capable of preventing faults being likely to occur in the servers in advance.