Log File Sequence Model for Computing Task Troubleshooting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Troubleshooting computing tasks based on error messages in log files is inefficient and ineffective due to the need to sift through large amounts of data, often identifying only the result of the problem rather than its source.
Innovation Solution
Creating a model of a successful log file by identifying invariants from multiple successful executions and comparing it to an anomalous log file to quickly identify discrepancies, using sequence alignment algorithms and trie structures to align and analyze log file sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to analyze error messages in log files, then troubleshooting can be performed, but it requires sorting through large amounts of data consuming time and computing resources
Solution Approach 1:
The system creates a model of successful log file execution sequences in advance by analyzing multiple successful log files and identifying invariant patterns. This preliminary model construction allows for rapid comparison against anomalous log files without re-analyzing entire log files from scratch, thus reducing troubleshooting time while maintaining accuracy.
Solution Approach 2:
The system extracts and isolates only the invariant sequences from successful log files that are relevant to the computing task execution. By removing irrelevant information and focusing only on the critical invariant patterns, the system reduces the amount of data that needs to be sorted through during troubleshooting, thereby improving efficiency and reducing time consumption.
2Measurement precision
If traditional error message analysis is used, then troubleshooting can be performed, but it may only identify the result of the problem rather than the source
Solution Approach 1:
The system segments the log file into discrete functional sequences and identifies invariant patterns within each segment. By analyzing the sequence of operations and comparing them against the model of successful execution, the system can pinpoint exactly where the anomaly occurs in the execution flow, thereby identifying the source of the problem rather than just the symptom.
Solution Approach 2:
The system uses feedback from comparing actual log execution against the pre-built model of successful execution to identify discrepancies. This feedback mechanism allows the system to detect not only that an error occurred but also to trace back through the execution sequence to identify the specific point where the actual execution deviated from the expected path, thus locating the error source.
3Reliability
If log files are analyzed to troubleshoot computing tasks, then problems can be identified, but large amounts of data must be sorted through consuming computing resources
Solution Approach 1:
The system performs preliminary analysis of multiple successful log files to build a compressed model of expected execution sequences. This pre-processing step creates a reference framework that can be quickly compared against anomalous log files, reducing the computing resources needed during actual troubleshooting while maintaining reliable error identification.
Solution Approach 2:
The system extracts and stores only the invariant patterns from successful log executions, discarding redundant and irrelevant information. This extraction creates a condensed representation of expected behavior that requires significantly fewer computing resources to analyze against anomalous logs, thereby reducing overall resource consumption while preserving troubleshooting effectiveness.
Data Source
AI summary
The disclosed computer-implemented method for troubleshooting computing tasks using log files may include (1) identifying multiple log files generated during successful executions of a computing task, (2) identifying an anomalous log file generated during an anomalous execution of the computing task, (3) creating a model of a successful log file for the computing task by (a) identifying invariants that represent matching sequences found in the same location within at least two successful log files and (b) storing each invariant in a node within the model, and (4) traversing, sequentially through the anomalous log file, matching sequences within the anomalous log file with nodes within the model until identifying at least one discrepancy between the anomalous log file and the model. Various other methods, systems, and computer-readable media are also disclosed.