Intelligent Infrastructure Operation Management System for Data Center Anomaly Detection
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Solution Overview
Problem
Conventional data centers face challenges in predicting and managing infrastructure failures, leading to service interruptions and high operating expenses due to inefficiencies in anomaly detection and pre-prediction analysis, which affects the quality of service (QoS).
Innovation Solution
An intelligent operation management system that utilizes a data collection analytics platform (DCAP) for real-time anomaly detection and abnormal traffic prediction, employing graphical visualization and natural language generation to provide augmented analytics, minimizing operator decision-making and ensuring stable QoS through pre-maintenance management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional data center operation management is used, then operator decision-making is required for anomaly detection and failure prediction, but this leads to long response time and service interruption
Solution Approach 1:
The system enables self-service through autonomous operation management where the infrastructure automatically detects anomalies, predicts failures, and performs maintenance without human intervention. The autonomous operation management system continuously monitors infrastructure status, detects deviations from normal operation, and executes pre-planned maintenance actions, eliminating the need for operator decision-making and significantly reducing response time.
Solution Approach 2:
The system implements continuous feedback loops through real-time monitoring of infrastructure resources. The autonomous operation management system collects operational data, analyzes it against predefined thresholds and patterns, and automatically triggers maintenance actions when anomalies are detected. This closed-loop feedback mechanism ensures rapid response to infrastructure issues without requiring human intervention.
2Reliability
If conventional anomaly detection methods are used, then manual analysis is required, but this results in insufficient detection capability and frequent service interruption
Solution Approach 1:
The autonomous operation management system provides multi-functional capabilities including real-time monitoring, anomaly detection, failure prediction, and automated maintenance execution. This universal system handles multiple infrastructure management tasks through a single integrated platform, improving service reliability while managing complexity through consolidation rather than multiplication of components.
Solution Approach 2:
The system performs preliminary actions by continuously analyzing operational data and predicting potential failures before they occur. The autonomous operation management system identifies trends and patterns that indicate impending infrastructure failures, allowing maintenance to be scheduled and executed proactively, thereby preventing service interruptions rather than reacting to them after they occur.
3Loss of energy
If pre-maintenance management is implemented, then operating expenses are reduced, but this requires advanced analytics capability and automated systems
Solution Approach 1:
The system replaces manual mechanical processes with automated digital systems. Instead of operators manually monitoring infrastructure and making maintenance decisions, the autonomous operation management system uses automated data collection, analysis, and execution mechanisms. This substitution of manual processes with automated systems reduces operating expenses while requiring the implementation of advanced automation capabilities.
Data Source
AI summary
An intelligent operation management apparatus for infrastructure may include a memory and a processor. Herein, the processor may be configured to: collect data by monitoring a resource of an operation target, perform an anomaly detection analysis by various methods of visualization using a graph for the collected data, perform an abnormal prediction analysis for the collected data, and perform pre-maintenance intelligent management based on a result of the anomaly detection analysis and a result of the abnormal prediction analysis. According to an apparatus and method for intelligent operation management of infrastructure, an effect of reducing operation expense and an effect of consecutively providing the quality of service (QoS) may be expected.