Log-Based Failure Diagnosis Using Syntactic Pattern Clustering
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Solution Overview
Problem
Diagnosing failures in complex enterprise systems is challenging due to the heterogeneity of system logs and the difficulty in extracting failure signatures from vast amounts of normal and event records, which can lead to propagation of failures across connected components and increased downtime.
Innovation Solution
A method and system that extract syntactic patterns from heterogeneous logs, cluster them into failure categories, and identify semantically unique patterns to detect system failures, allowing for corrective actions to be taken automatically, using unsupervised learning and automata models to understand failure mechanisms and prevent further propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer systems collect logs to help with diagnosis, then the ability to diagnose failures is improved, but the complexity of processing and analyzing the logs increases
Solution Approach 1:
The patent segments the complex log analysis task into distinct phases: log collection from multiple sources, syntactic pattern extraction, semantic pattern identification, and failure signature matching. This segmentation transforms the overwhelming task of analyzing vast amounts of heterogeneous logs into manageable, sequential processing steps, resolving the contradiction between diagnosis accuracy and processing complexity
Solution Approach 2:
The patent introduces syntactic patterns and semantic patterns as intermediary representations between raw logs and failure diagnoses. These patterns act as mediators that simplify the complex relationship between heterogeneous log formats and failure signatures, enabling accurate diagnosis without directly processing the full complexity of raw logs
2Loss of information
If computer systems collect comprehensive logs including normal records and event records, then the information available for diagnosis is improved, but the difficulty of extracting correct failure signatures increases
Solution Approach 1:
The patent extracts syntactic patterns from the comprehensive log data by identifying and isolating recurring structural elements. This extraction process separates the signal (failure signatures) from the noise (normal operational records), making it possible to identify correct failure signatures even within vast amounts of heterogeneous log data
Solution Approach 2:
The patent transforms log data through parameter changes by converting raw log entries into syntactic patterns with specific structural parameters, then further transforming them into semantic patterns with meaningful parameters. This multi-stage parameter transformation makes failure signature extraction feasible by progressively simplifying the data representation
3Reliability
If enterprise systems maintain high availability with minimum interruption, then system reliability is improved, but the need for rapid failure detection and response increases system complexity
Solution Approach 1:
The patent performs preliminary actions by pre-extracting syntactic patterns from logs and pre-identifying semantic patterns before failures occur. This preliminary processing creates a ready-to-use pattern library that enables rapid failure detection when issues arise, maintaining high availability without requiring complex real-time analysis systems
Solution Approach 2:
The patent implements self-service through automated failure detection and diagnosis that operates without human intervention. The system automatically collects logs, extracts patterns, identifies failures, and generates diagnoses, reducing the complexity of manual monitoring while improving system reliability through consistent, rapid response
Data Source
AI summary
Methods and systems for system failure diagnosis and correction include extracting syntactic patterns from a plurality of logs with heterogeneous formats. The syntactic patterns are clustered according to categories of system failure. A single semantically unique pattern is extracted for each category of system failure. The semantically unique patterns are matched to recent log information to detect a corresponding system failure. A corrective action us performed responsive to the detected system failure.