Causal Map for Data Center Root Cause Analysis
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Solution Overview
Problem
Traditional root cause analysis techniques in data centers are inefficient as they rely on predefined causality models and manual investigation, failing to isolate the real cause of complex IT issues due to their limitations in correlating disparate events across IT infrastructure.
Innovation Solution
A method and apparatus using a causal map to identify the root cause of data center management problems by building a problem domain and inferring causal events through a structured analysis process that correlates events across IT infrastructure, providing an intuitive visual representation and simplifying the problem analysis approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional predefined causality models and manual investigation are used for root cause analysis, then the analysis process can be performed with simple tools, but the troubleshooting time and efficiency are excessively long
Solution Approach 1:
The system performs preliminary actions by automatically collecting, storing, and organizing event data from multiple IT infrastructure components before problems occur. Event correlation rules are pre-configured to define causal relationships between different event types, enabling the system to quickly retrieve and analyze relevant events when a problem is detected, rather than manually investigating from scratch.
Solution Approach 2:
The patent introduces an event correlation engine as an intermediary between raw event data and root cause analysis. This engine automatically correlates events from different IT components using predefined rules, creating a structured representation of causal relationships that simplifies the analysis process and eliminates the need for manual investigation of disparate event sources.
2Reliability
If manual investigation and troubleshooting are performed by analysts, then flexible problem-solving can be achieved, but the process becomes highly dependent on individual skills and is inconsistent
Solution Approach 1:
The system implements self-service by automatically performing event collection, correlation, and root cause identification without requiring manual analyst intervention. The event correlation engine autonomously applies predefined rules to event data, and the root cause analysis module automatically determines the underlying cause based on correlated events, eliminating dependency on individual troubleshooter skills while ensuring consistent, reliable analysis results.
Solution Approach 2:
The system incorporates feedback mechanisms where the root cause analysis results are fed back into the event correlation process. The system continuously monitors IT infrastructure events, compares them against correlated event patterns, and automatically adjusts analysis based on the causal relationships identified through event correlation, ensuring consistent and reliable problem diagnosis independent of human operator variability.
3Measurement precision
If comprehensive event data from multiple IT infrastructure components is collected and analyzed, then accurate root cause identification can be achieved, but the complexity of correlating disparate events increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the complex event correlation process into distinct modular components: event collection modules for different IT infrastructure types (network, storage, compute), an event correlation engine that processes events in standardized formats, and a root cause analysis module. This segmentation allows each component to handle specific event types independently, reducing the overall complexity of correlating disparate events while maintaining comprehensive coverage for accurate root cause identification.
Solution Approach 2:
The system changes parameters by transforming diverse event data from multiple IT infrastructure components into a standardized event format with uniform attributes and structures. The event correlation engine uses predefined correlation rules that operate on these standardized parameters, enabling accurate identification of causal relationships across different event types without being overwhelmed by the original complexity and heterogeneity of the source data.
Data Source
AI summary
Example embodiments of the present invention provide a method and an apparatus for problem analysis using a causal map. The method includes building a problem domain corresponding to a datacenter management problem and building a causal map corresponding to the problem domain. A causal event of the datacenter management problem then may be inferred according to the causal map.