Neural Network Data Center Anomaly Detection
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Solution Overview
Problem
Data centers face challenges in managing complex systems with numerous sensors, leading to difficulties in detecting anomalies and failures in real-time due to the overwhelming amount of data and high likelihood of false positives/negatives, making it hard for operators to extract valuable insights.
Innovation Solution
The implementation of machine learning and neural networks to create models that enhance risk management and pinpoint failures, including in uninstrumented parts of the data center, by encoding data center representations into neural networks to detect anomalies across systems, subsystems, and sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based monitoring systems are used to track numerous sensors in data centers, then coverage of system monitoring is improved, but false positives and negatives increase making it difficult to extract valuable insights
Solution Approach 1:
The patent introduces an AI-based intermediary system that processes sensor data through multiple layers including embedding layers, graph convolutional networks, and attention mechanisms. This intermediary layer transforms raw sensor readings into meaningful anomaly detections by learning complex patterns and relationships, thereby reducing false positives and negatives while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent replaces traditional mechanical threshold-based monitoring systems with an AI-driven neural network system. Instead of using fixed thresholds and simple comparison logic, the system employs machine learning models that adaptively learn normal and abnormal patterns, substituting rigid mechanical detection mechanisms with flexible intelligent algorithms that significantly reduce false detections.
2Quantity of substance
If more sensors are deployed to monitor all critical subsystems, then system coverage is improved, but data complexity and difficulty of managing overwhelming amounts of data increase
Solution Approach 1:
The patent segments the data management process into distinct functional layers: data collection layer, processing layer, and decision layer. The graph neural network architecture further segments the monitoring scope into different subsystems (power distribution, cooling, IT equipment) with specialized processing for each, making the overall complex system manageable through modular organization.
Solution Approach 2:
The patent introduces an AI-based intermediary system that processes sensor data through multiple layers including embedding layers, graph convolutional networks, and attention mechanisms. This intermediary layer transforms raw sensor readings into meaningful anomaly detections by learning complex patterns and relationships, thereby reducing false positives and negatives while maintaining comprehensive monitoring coverage.
3Productivity
If traditional design approaches are used for data centers, then design process simplicity is maintained, but ability to optimize for power efficiency, thermal management, and risk mitigation is limited
Solution Approach 1:
The patent applies preliminary action by using AI models to predict future system states, failures, and performance bottlenecks before they actually occur. The system performs what-if analyses and risk assessments in advance, allowing designers to optimize configurations proactively rather than reactively, thereby improving power efficiency and thermal management while managing design complexity through forward-looking simulations.
Data Source
AI summary
In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.