Memory Failure Prediction Model Using Data Center Operational Attributes
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Solution Overview
Problem
Current memory failure prediction techniques in data centers primarily rely on memory controller components and do not consider external operational parameters of data centers or data center assets, which can contribute to memory failures.
Innovation Solution
A method and system for predicting memory failures in data centers by receiving data center and asset data, providing it to a memory failure prediction model, and training the model to consider both memory component telemetry and operational attributes of data center assets and the data centers themselves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If memory failure prediction relies only on memory controller components, then the prediction system is simple to implement, but the prediction accuracy is insufficient because external operational parameters are not considered
Solution Approach 1:
The patent combines memory controller telemetry data with data center operational parameters (power, cooling, workload) into a unified prediction model. This merging of multiple data sources enables comprehensive failure prediction while maintaining system manageability through centralized model training and deployment.
Solution Approach 2:
The prediction system serves multiple functions: it analyzes memory controller data, processes operational parameters, trains machine learning models, and generates failure predictions. This multi-functional approach consolidates various monitoring and analysis tasks into a single comprehensive system.
2Measurement precision
If multiple data center operational parameters are collected and analyzed, then the prediction accuracy improves, but the data processing complexity and computational resources increase
Solution Approach 1:
The system performs preliminary data collection and preprocessing of operational parameters before feeding them into the prediction model. Historical data is gathered and prepared in advance, enabling the model to train on comprehensive datasets without overwhelming computational complexity during real-time prediction.
Solution Approach 2:
The patent transforms raw operational parameters into meaningful features suitable for machine learning analysis. By changing the representation of parameters (e.g., aggregating time-series data, normalizing values), the system reduces computational complexity while preserving predictive information.
3Reliability
If comprehensive training data from multiple sources is used, then the model generalization capability improves, but the training time and computational resources increase
Solution Approach 1:
The system uses a representative subset of training data that captures the essential patterns of memory failures across different operational conditions. By selecting critical data samples rather than processing all available data, the model achieves good generalization with reduced training time and computational resources.
Data Source
AI summary
A system, method, and computer-readable medium for performing a data center monitoring and management operation, The data center monitoring and management operation includes receiving data center data for a data center, the data center data comprising data center memory associated data; receiving data center asset data for a plurality of data center assets, the data center asset data comprising data center asset memory associated data; providing the data center memory associated data and the data center asset memory associated data to a memory failure prediction model; and, training the memory failure prediction model using the data center memory associated data and the data center asset memory associated data.