Memory Fault Prediction Using Multi-Log Feature Modeling
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Solution Overview
Problem
Current methods for predicting memory failures in servers rely on simple summation of error checking and correcting (ECC) counts, leading to low accuracy, and existing systems fail to provide comprehensive real-time failure prediction and health assessment.
Innovation Solution
A method involving the acquisition of multiple types of log data, including memory error information address data, performing feature engineering construction, splicing these data tables, and using a pre-trained failure prediction model to improve accuracy and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple summation of ECC counts is used for memory failure prediction, then the prediction method is simple and easy to implement, but the prediction accuracy is very low
Solution Approach 1:
The patent segments the memory log data into multiple feature dimensions including ECC error information, memory access patterns, temperature data, and workload characteristics. Each dimension is processed separately to extract specific features, which are then combined for comprehensive prediction. This segmentation allows the system to capture diverse failure indicators while maintaining manageable processing complexity.
Solution Approach 2:
The patent transitions from one-dimensional ECC counting to multi-dimensional feature analysis by incorporating temporal patterns, spatial distribution of errors, environmental factors, and workload conditions. This dimensional expansion enables the prediction model to capture complex failure modes that cannot be detected by simple ECC summation alone.
2Measurement precision
If multiple types of log data are collected and processed through feature engineering, then the prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The patent implements pre-trained failure prediction models that are prepared in advance with extensive training data. These models encapsulate complex feature engineering and analysis capabilities that were developed beforehand, allowing the runtime system to simply input log data and receive predictions without performing complex processing itself. This shifts complexity from runtime to offline model training.
Solution Approach 2:
The patent introduces a trained prediction model as an intermediary between raw log data and failure predictions. This model acts as a mediator that has already learned the complex relationships between various log features and failure patterns, thereby simplifying the runtime system's task while maintaining high prediction accuracy through the model's learned knowledge.
3Adaptability or versatility
If comprehensive feature engineering is performed on multiple log data types, then the generalization capability is improved, but the data processing time increases
Solution Approach 1:
The patent performs comprehensive feature engineering and model training in advance using diverse training data that covers various failure scenarios and operating conditions. This preliminary action creates a robust prediction model with strong generalization capability that can handle different memory types and failure modes. At runtime, the pre-trained model quickly processes new log data without requiring extensive feature engineering, thus reducing processing time while maintaining generalization ability.
Data Source
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AI summary
The present application relates to a method for predicting a memory fault, and an electronic device and a computer-readable storage medium. The method for predicting a memory fault comprises: acquiring a plurality of types of log data of a memory under test, wherein the plurality of types of log data at least comprise memory error information address data; performing feature engineering construction according to the plurality of types of log data, so as to obtain feature data tables respectively corresponding to the plurality of types of log data; combining the feature data tables respectively corresponding to the plurality of types of log data, so as to obtain a combined feature data table; and obtaining a fault prediction result of the memory under test according to the combined feature data table and a pre-trained fault prediction model, wherein the fault prediction model is obtained by performing training according to a pre-collected training data set, and samples in the training data set comprise the plurality of types of log data of a plurality of types of memories.