Server Fault Analysis System Using Event Log Spotlights
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Solution Overview
Problem
Complex server misconfigurations in data storage systems are difficult to diagnose and resolve, leading to significant downtime and inefficiencies in customer support, as support engineers spend excessive time searching through large volumes of diagnostic messages to identify root causes and apply corrective commands.
Innovation Solution
A server fault analysis system that highlights relevant log messages (root cause spotlights) and correlated remedial commands (resolution spotlights) using data mining and statistical techniques, reducing the time needed to identify and resolve misconfigurations by presenting concise sets of messages and commands to support engineers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If support engineers manually search through large volumes of diagnostic messages to identify root causes, then they can diagnose server misconfigurations, but the process takes excessively long periods of time
Solution Approach 1:
The patent segments the large volume of diagnostic messages into structured event logs with specific categories and types. By organizing log data into hierarchical segments (log files → event logs → individual events → specific fields), the system enables targeted analysis of relevant portions rather than manual searching through entire log volumes, thus maintaining diagnostic accuracy while reducing time to identify root causes.
Solution Approach 2:
The patent introduces an intermediary fault analysis system that acts as a mediator between the raw diagnostic messages and the support engineers. This system automatically parses, correlates, and analyzes event logs using predefined fault patterns and machine learning models, presenting processed findings to engineers. The intermediary handles the time-consuming manual search task while preserving diagnostic precision through systematic analysis methods.
2Measurement precision
If support engineers manually analyze event logs to identify root causes, then they can diagnose misconfigurations, but the complexity of the process increases
Solution Approach 1:
The patent implements self-service capabilities where the fault analysis system automatically performs log parsing, event correlation, and root cause identification without requiring manual analysis procedures. The system uses predefined fault patterns, automated correlation rules, and machine learning models to independently analyze event logs and generate diagnostic findings, thereby maintaining high detection accuracy while eliminating the complexity of manual analysis processes for support engineers.
Solution Approach 2:
The patent transforms the complex qualitative analysis process into structured parameter-based evaluation. By defining specific event log fields, correlation parameters, and fault pattern parameters, the system converts the ambiguous manual analysis process into a standardized parameter-matching process. This parameterization maintains diagnostic precision through systematic evaluation while reducing process complexity through automated parameter comparison and pattern matching.
3Loss of information
If the system presents all diagnostic messages to support engineers, then complete information is available, but the volume of information makes it difficult to identify relevant root cause messages
Solution Approach 1:
The patent extracts only the relevant root cause messages from the complete set of diagnostic messages using automated analysis. The system parses event logs, identifies events matching fault patterns, and extracts specific fields (event type, severity, source component, timestamp) that indicate root causes. This extraction process maintains information completeness by preserving all relevant diagnostic data while removing irrelevant messages, thereby improving ease of operation by presenting only the extracted root cause information to support engineers.
Solution Approach 2:
The patent performs preliminary analysis and filtering of diagnostic messages before presenting them to support engineers. The system pre-processes event logs by parsing, categorizing, and correlating events against known fault patterns, and pre-identifies potential root causes. This preliminary action maintains complete information availability by preserving all diagnostic data in the background while preparing a filtered, organized presentation of relevant root cause messages, thus improving ease of operation by eliminating the need for engineers to sift through incomplete or unprocessed information.
Data Source
AI summary
A method and system for server fault analysis are described. A server configuration analyzer retrieves event logs collected from a server with a fault such as a misconfiguration. These event logs can then be analyzed using metrics to identify one or more messages in the logs that indicate a cause of the fault on the server. Once the messages are identified, the server configuration analyzer retrieves remedial commands that may be used to correct the fault from a database that stores remedial commands correlated with event log messages. The server configuration analyzer can then present the messages and remedial commands to a user to assist with identifying the cause of the fault and correcting it.