Causal Graph Root Cause Analysis for IT Network Anomalies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fault monitoring and analysis tools in complex IT networks are inefficient in identifying the root cause of anomalies due to their inability to utilize causal relationships between system components, relying heavily on manual processes and correlation-based methods which are time-consuming and resource-intensive.
Innovation Solution
Implementing a root cause analysis system that uses causal graphs to automatically generate a prioritized list of possible causes for observed anomalies by traversing the causal graph structure from the affected node to the source, leveraging domain expertise and semi-supervised tools for environmental mapping and anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis procedures are used to identify root causes of anomalies, then analysts can utilize their working experience and knowledge about system relationships, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent introduces an intermediary system consisting of a knowledge base and analysis engine that mediates between the complex IT network data and the analyst. The knowledge base stores predefined relationships between system components, and the analysis engine automatically queries and analyzes these relationships to identify root causes, reducing the time required while maintaining accuracy through structured domain knowledge
Solution Approach 2:
The system performs preliminary action by pre-defining and storing knowledge about system relationships, component dependencies, and fault patterns in the knowledge base before anomalies occur. When an anomaly is detected, the system can immediately query this pre-prepared knowledge to rapidly identify potential root causes without requiring analysts to manually explore system relationships from scratch
2Productivity
If correlation-based methods are used to analyze system relationships, then tools can automatically process data, but they cannot capture non-trivial causal relationships between components
Solution Approach 1:
The knowledge base acts as an intermediary that bridges the gap between automated correlation analysis and accurate causal relationship detection. It stores expert-defined causal relationships and domain knowledge that enhance the precision of relationship detection while maintaining automated processing through structured queries and analysis algorithms
Solution Approach 2:
The system changes the parameter of relationship representation from simple correlation coefficients to structured causal relationships with defined confidence levels and dependency types. This allows the system to maintain automation while capturing non-trivial causal relationships by transforming how relationships are modeled and analyzed
3Ease of operation
If standard monitoring tools are used to visualize system information, then tools can display time-series data and anomalies, but they do not fully utilize relationships between system components
Solution Approach 1:
The system merges the visualization capabilities of standard monitoring tools with the causal analysis capabilities of the knowledge base. The interface combines time-series data visualization with overlaid causal relationship information, allowing analysts to see both the visual representation of system behavior and the contextual relationships between components simultaneously
Solution Approach 2:
The system adds another dimension to the visualization by overlaying causal relationship data onto the time-series visualization. This additional dimension provides context about component relationships without obscuring the temporal patterns, allowing analysts to perceive both dimensions of information in an integrated view
Data Source
AI summary
Embodiments for finding a root cause of an anomaly in a network environment by representing assets in the network environment as respective nodes in a causal graph, wherein the nodes have a measurable quality that can be tracked and arcs between pairs of nodes represent causal relationships between nodes of the node pairs designating source nodes as processes at the top of a hierarchy of tracked processes, and sink nodes as processes at the bottom of the hierarchy and having characteristics of interest in the environment; detecting anomalies in the tracked processes embodied in the sink nodes; traversing the causal graph in a reverse order from a node in which an outlier is detected; and analyzing nodes along the traversal path to identify a node of the highest hierarchy that shows unusual behavior as the root cause.