Neural Network Failure Prediction Using Code Flow Tensors
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Solution Overview
Problem
Predicting failures in data storage systems and applications is challenging due to complex code flow patterns and the difficulty in correlating counter values with error states in real-time, leading to inefficient risk assessment and debugging.
Innovation Solution
A method involving the use of neural networks that receive and analyze data sets with counter values to generate images, which are then labeled with state information, allowing the network to recognize error states and predict future transitions, utilizing a combination of active and idle neural networks for continuous learning and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to monitor and analyze code flow patterns and counter values, then system operation can be maintained, but failure prediction accuracy remains low and debugging efficiency is poor
Solution Approach 1:
The patent replaces traditional mechanical monitoring and analysis methods with a neural network-based predictive system. The neural network automatically processes code flow patterns and counter values to predict failures, substituting manual analysis and traditional algorithms with an intelligent system that achieves higher prediction accuracy and provides actionable insights for debugging.
Solution Approach 2:
The patent creates visual representations (images) of code flow patterns and counter values that can be processed by the neural network. These visual copies of the system state enable the network to recognize patterns and predict failures more effectively than raw data alone, improving both prediction accuracy and debugging efficiency.
2Reliability
If real-time analysis of code flow patterns and counter values is performed using traditional algorithms, then system monitoring is maintained, but the complexity of correlating data with error states increases
Solution Approach 1:
The patent replaces complex traditional algorithms with a neural network that automatically handles the correlation between code flow patterns, counter values, and error states. The neural network's learning capability simplifies the data correlation process while maintaining reliable system monitoring, reducing the complexity burden on the system architecture.
Solution Approach 2:
The patent introduces visual representations of code flow patterns as an intermediary between raw counter values and error state correlation. This intermediate visual format makes the data more amenable to neural network processing and simplifies the overall correlation complexity while preserving monitoring reliability.
3Measurement precision
If comprehensive data collection from multiple sources is performed, then prediction completeness is improved, but the time required for processing and analysis increases
Solution Approach 1:
The patent replaces traditional sequential processing methods with a neural network that can process multiple data sources simultaneously. The neural network's parallel processing capability allows it to analyze code flow patterns, counter values, and other system data from multiple sources without proportionally increasing processing time, thereby improving prediction completeness efficiently.
Solution Approach 2:
The patent performs preliminary processing of data into visual representations before neural network analysis. This preprocessing step organizes comprehensive data from multiple sources into a format optimized for neural network consumption, reducing the actual processing time required while maintaining prediction completeness.
Data Source
AI summary
Techniques for predicting states may include: receiving data sets of counter values, wherein each counter values denotes a number of times a particular code flow point associated with the counter value is executed at runtime during a specified time period; receiving images generated from the data sets; labeling each of the images with state information, wherein first state information associated with a first image indicates that the first image is associated with a first error state of a system or an application; training a neural network using the images to recognize the first state; receiving a next image generated from another data set; and predicting, by the neural network and in accordance with the next image, whether the system or the application is expected to transition into the first state. In at least one embodiment, the foregoing processing may optionally use matrices generated from the data sets rather than images.