Log Template Anomaly Detection Using Golden Signal Baselines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing log analysis systems struggle to efficiently detect anomalies in log files, particularly in cloud computing environments, due to the vast volume and complexity of log data, making it difficult to identify meaningful patterns and deviations from normal behavior.
Innovation Solution
A machine learning-based approach is employed to identify log templates within log files, filter them using a golden signal dictionary, and compare instance counts to baseline counts to detect anomalies, utilizing machine learning models to analyze log templates and golden signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional log analysis methods are used, then the system can process log files, but it cannot efficiently detect anomalies in the vast volume and complex log data
Solution Approach 1:
The patent segments log data into templates and categorizes them using a golden signal dictionary. By breaking down the complex log data into standardized template instances and filtering through predefined categories (error, warning, info, etc.), the system reduces the effective data volume that needs manual analysis while maintaining comprehensive coverage of the original log content.
Solution Approach 2:
The patent extracts meaningful patterns from log data by identifying template instances and their occurrences. Using machine learning models, the system extracts and isolates anomalous patterns from the vast log data, separating them from normal operational noise. This extraction process enables focused analysis on only the relevant anomaly information rather than processing every individual log entry.
2Measurement precision
If machine learning models are used to identify log templates, then anomaly detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent employs preliminary action by pre-training machine learning models to recognize log templates and categorizing them into golden signal categories before actual anomaly detection occurs. The system pre-establishes baseline counts for each template category and prepares the golden signal dictionary in advance. This preliminary processing enables accurate anomaly detection during operation without requiring complex real-time decision-making, thus improving accuracy while managing system complexity.
3Reliability
If all log templates are analyzed, then complete log coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and filters log templates through a golden signal dictionary that identifies only the relevant template categories needing analysis. By extracting and focusing on specific golden signal categories (error, warning, critical, etc.) rather than processing every possible log template equally, the system maintains reliable coverage of important events while significantly reducing processing time and computational resources.
Solution Approach 2:
The patent applies partial action by analyzing only the necessary subset of log templates that map to golden signal categories, rather than exhaustively processing all possible log templates. The machine learning models identify and prioritize templates that are most likely to contain anomalies, allowing the system to achieve reliable anomaly detection by focusing on the most critical log patterns rather than uniformly processing everything.
Data Source
AI summary
An example operation may include one or more of identifying instances of different log templates included in a log file based on execution of a machine learning (ML) model on the log file, filtering the different log templates based on a golden signal dictionary to identify log templates that correspond to golden signals, respectively, determining a count of instances of the log templates within the log file, and detecting an anomaly within the log file based on a comparison of the count of the instances of the log templates to baseline counts of the log templates.