ML Log Analysis for Server Incident Root Cause Identification
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Solution Overview
Problem
Support engineers face challenges in efficiently identifying and resolving customer server issues due to the complexity of analyzing large volumes of message codes in log files, which leads to increased time and resource consumption and customer dissatisfaction.
Innovation Solution
A method employing machine learning processes, specifically Sequence Graph Transformation and Principal Component Analysis, is used to compare customer incident log files with internal logs from validation test case runs, enabling faster identification of root causes and recommended solutions by leveraging a knowledge base that mimics the customer environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If support engineers manually analyze customer server log files to identify root causes, then they can understand the issue and apply knowledge to determine possible root causes, but the process consumes significant time and resources leading to customer dissatisfaction
Solution Approach 1:
The patent creates a virtual copy of the customer's server environment by generating synthetic log files that replicate the structure, format, and characteristics of actual customer logs. This copy is then used for machine learning training and testing, enabling the system to learn from simulated data without requiring access to sensitive customer information. The virtual environment accurately mirrors real-world scenarios while eliminating time-consuming manual analysis.
Solution Approach 2:
The system performs preliminary actions by pre-generating extensive sets of synthetic log files representing various failure scenarios and system states before actual customer issues occur. Machine learning models are trained in advance on this pre-prepared data, so when a real customer issue arises, the model is already equipped to quickly analyze and diagnose the problem without needing to learn from scratch during the incident response.
2Reliability
If support engineers manually reproduce issues to identify resolutions, then they can verify possible root causes and find solutions, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent replaces the mechanical process of manual issue reproduction with an automated machine learning system. Instead of support engineers manually recreating customer environments and testing hypotheses, the ML model automatically analyzes log patterns, identifies root causes, and recommends resolutions. This substitution of mechanical manual processes with automated intelligent systems dramatically increases productivity while maintaining or improving resolution reliability through consistent, data-driven analysis.
Solution Approach 2:
The system introduces an intermediary machine learning model that acts as a mediator between customer log files and support engineers. The ML model processes the complex log data and translates it into actionable insights, serving as an intelligent intermediary that bridges the gap between raw data and human decision-making. This intermediary handles the time-consuming analysis work while providing reliable, consistent recommendations to support engineers.
3Loss of time
If the knowledge base accurately mimics the customer environment, then the system can identify root causes faster, but creating and maintaining such a knowledge base requires significant resources
Solution Approach 1:
The patent creates simplified virtual copies of customer server environments through synthetic log files that capture essential failure patterns and system behaviors. These copies replicate the critical characteristics needed for diagnostic accuracy without requiring complete duplication of complex customer systems. The synthetic data maintains the structural and behavioral properties of real logs while being generated through controlled processes, enabling fast root cause identification with reduced system complexity.
Solution Approach 2:
The system applies parameter changes by transforming real customer log data into synthetic versions with modified characteristics. The synthetic logs preserve key diagnostic parameters and failure patterns while varying non-essential parameters to create diverse training scenarios. This parameter transformation approach enables the knowledge base to handle multiple customer environments with a single unified system, reducing complexity while maintaining accuracy across different configurations.
Data Source
AI summary
One example method includes acquiring data from a knowledge base that includes message codes indicating conditions that occurred during performance of one or more test runs of a computing operation, and the message codes are included in message code sequences, processing the data by mapping the message codes, and message codes included in a customer issue log, to codes that are readable by a machine learning process, transforming the data to generate an output that comprises, for each message code sequence, relationships between each of the message codes in that message code sequence, extracting features from the transformed data, and the extracting generates multiple datasets that include the features, performing a similarity comparison by comparing a customer field issue with the datasets, and based on the similarity comparison, identifying and recommending a solution to the customer field issue.