Log-Based Failure Signature Generation
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Solution Overview
Problem
Current log-based computer system fault diagnosis methods are inefficient in generating failure signatures, particularly when dealing with heterogeneous logs from different encoding sources, and often require user-defined parameters and significant failure data, making it difficult to classify and predict system failures accurately.
Innovation Solution
A system and method that automatically generates failure signatures by converting log data into log pattern sequences, computing a global set of patterns, extracting seed patterns, and creating a log pattern grammar representation to identify unique failure categories, allowing for fault diagnosis without user-defined parameters and with limited failure data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log-based fault diagnosis methods are used, then user-defined parameters and significant failure data are required, but this makes it difficult to classify and predict system failures accurately and efficiently
Solution Approach 1:
The system automatically generates failure signatures from log data without requiring user-defined parameters. The signature generation process is self-driven, using the log patterns themselves to create diagnostic signatures, eliminating the need for manual parameter configuration and expert knowledge input.
Solution Approach 2:
The system extracts seed patterns from log data by computing a global set of patterns and identifying unique failure category patterns. This extraction process isolates the essential diagnostic information from heterogeneous logs, creating compact failure signatures that capture the core failure characteristics without requiring extensive raw data.
2Adaptability or versatility
If heterogeneous logs from different encoding sources are processed, then more comprehensive failure coverage is achieved, but processing efficiency decreases
Solution Approach 1:
The system processes heterogeneous logs from multiple sources with different encodings using a unified pattern extraction approach. The global pattern computation and seed pattern extraction mechanisms work universally across different log formats, making the system adaptable to various log sources while maintaining efficient processing through standardized pattern recognition.
Solution Approach 2:
The system transforms heterogeneous log data into a standardized pattern representation by changing the parameter form from raw log entries to abstracted log pattern sequences. This parameter transformation enables efficient processing of diverse log sources by converting them into a common diagnostic language that can be uniformly analyzed.
3Measurement precision
If significant failure data is collected for analysis, then more accurate failure signatures can be generated, but the time and resources required for data collection and processing increase
Solution Approach 1:
The system performs preliminary pattern extraction and seed pattern identification from available log data to generate failure signatures. By computing the global pattern set and extracting seed patterns in advance, the system prepares diagnostic capabilities without requiring extensive additional data collection, enabling timely fault diagnosis with limited historical failure data.
Data Source
AI summary
Systems and methods for automatically generating failure signatures in a computer system for performing computer system fault diagnosis are provided. The method includes receiving log data, converting each log in the log data into a collection of log pattern sequences including one or more log pattern sequences corresponding to one or more respective failure categories associated with the computer system, generating a collection of seed patterns by computing a global set of patterns from the collection of log pattern sequences, and extracting the collection of seed patterns from the global set of patterns, generating a log pattern grammar representation for each of the one or more log pattern sequences, generating a failure signature for each of the one or more failure categories based on the log pattern grammar representation and the collection of seed patterns, and employing the failure signatures to perform computer system fault diagnosis on new log data.