Multi-Dimension Information Correlation for DAE Root Cause Identification
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Solution Overview
Problem
In data storage systems, identifying the root cause of multiple disk failures in a data disk array enclosure (DAE) is complex due to various contributing factors such as temperature, power instability, and human errors, making it difficult to perform effective triage.
Innovation Solution
A method that leverages multi-dimension information, including historical drive data, storage topology, and system-level events, to automatically correlate and deduce the root cause of DAE-context issues using a predetermined algorithm, generating a report with probability indicators for each potential cause.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual triage procedure is used to identify root cause of disk failures, then detailed analysis can be performed, but the process becomes complex and time-consuming due to multiple contributing factors
Solution Approach 1:
The system performs preliminary actions by proactively collecting and storing multi-dimensional information (temperature data, power supply status, event logs, drive configurations) before failures occur. This pre-prepared information enables rapid root cause analysis when failures happen, eliminating the need for time-consuming manual data gathering during triage while maintaining high identification accuracy.
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a mediator between the complex multi-factor failure environment and the user. This intermediary correlates multiple data dimensions (temperature correlations, power supply relationships, event timing) to automatically identify root causes, reducing both the complexity perceived by users and the time required for accurate identification.
2Measurement precision
If comprehensive multi-dimension information is collected and correlated, then root cause identification accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex analysis task into distinct modular components: temperature data collection module, power supply status module, event log analysis module, and correlation engine. Each module handles a specific aspect of data collection or processing, making the overall complex system manageable through clear separation of concerns while maintaining comprehensive multi-dimensional analysis capability.
Solution Approach 2:
The system implements self-service through automated information collection and correlation algorithms that autonomously analyze multi-dimensional data without requiring manual intervention. The automated root cause identification process correlates temperature patterns, power events, and drive failures independently, reducing the operational complexity burden on users while delivering high-accuracy results.
Data Source
AI summary
In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.