LSTM Log Analysis for Data Storage Anomaly Detection
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Solution Overview
Problem
The complexity of data storage systems generates a large volume of logs, making it time-consuming for engineers to identify and correct operational issues, necessitating an intelligent log analysis technology that can automatically narrow down errors.
Innovation Solution
The integration of natural language processing (NLP) and deep learning technologies, specifically using vectorization and Long Short-Term Memory (LSTM) neural networks, to analyze system logs and generate anomaly reports, reducing manual effort and response time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If engineers manually examine system logs to identify operational issues, then they can detect and correct errors, but the process becomes time-consuming and inefficient given the large volume of logs generated by complex data storage systems
Solution Approach 1:
The patent replaces manual mechanical examination of logs with an automated neural network system. The LSTM-based model automatically processes log messages, vectorizes them, and identifies anomalies without human intervention, thereby eliminating the time-consuming manual review process while maintaining or improving error detection capability
Solution Approach 2:
The patent introduces an intermediary neural network processing layer between the raw logs and the engineer. This intermediary system performs vectorization, sequence generation, and anomaly detection, transforming the raw log data into actionable insights that engineers can quickly review, thus reducing the time engineers spend directly examining logs
2Adaptability or versatility
If the data storage system increases complexity to meet growing demand, then it can provide more functionality, but it generates a larger volume of logs that are more difficult to analyze
Solution Approach 1:
The patent transforms the parameter representation of log data by converting raw text logs into vectorized numerical representations. This parameter transformation enables the neural network to process and analyze logs efficiently, handling the increased log volume generated by complex systems without proportionally increasing analysis difficulty
Solution Approach 2:
The patent segments the log analysis process into distinct computational stages: vectorization of individual log messages, generation of sequences using LSTM, and final anomaly detection. This segmentation allows the system to handle large volumes of logs from complex systems by processing them through specialized sub-routines rather than attempting monolithic analysis
Data Source
AI summary
System logs are processed to identify and report anomalies in execution of processes of a log-generating system such as a data storage system. Log messages of system logs are vectorized to generate log-message vectors; long short-term memory (LSTM) neural network processing is applied to the log-message vectors to generate an LSTM output sequence representing a production flow of the processes; and second-level neural network processing is applied to a combination of the LSTM output sequence and a training sequence to generate an analysis sequence containing a representation of anomalies in the production flow of the processes, where the training sequence is generated from a non-anomalous training flow. An anomaly report is generated and provided to a report consumer for taking further action with respect to the anomalies represented in the analysis sequence.