Root Cause Grouping in Distributed Systems via Topology Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying causal relationships between abnormal operating conditions in complex, large-scale environments are inefficient, as they struggle to handle large graph structures and often produce too many root cause candidates for timely analysis.
Innovation Solution
A system that deploys heterogeneous monitoring agents to create a unified topology model, integrating topology-related, resource utilization, and transaction trace data to identify and group causally related abnormal operating conditions based on shared resource and code execution dependencies, using vertical and horizontal grouping and ranking mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use topology data and heuristics to identify causal dependencies and create root cause candidate graphs, then causal relationship identification accuracy is improved, but the complexity of processing large graph structures increases and root cause candidates become too numerous for timely analysis
Solution Approach 1:
The patent segments the complex graph of causally related abnormal operating conditions into manageable groups by introducing a grouping mechanism that clusters nodes based on shared resources and code execution dependencies. This segmentation transforms the large graph into smaller, more manageable sub-graphs that can be processed more efficiently while maintaining causal relationship accuracy.
Solution Approach 2:
The patent extracts and removes redundant information from the graph structure by filtering out abnormal operating conditions that do not meet specific criteria (such as those without shared resources or code dependencies). This extraction reduces the graph size and complexity while preserving the essential causal relationships needed for root cause analysis.
2Measurement precision
If existing methods analyze large sets of causally related abnormal operating conditions to identify root causes, then comprehensive root cause identification is improved, but the time required for analysis increases substantially
Solution Approach 1:
The patent performs preliminary grouping and filtering actions before conducting root cause analysis. By pre-organizing abnormal operating conditions into groups based on shared resources and code dependencies, and by pre-filtering out less relevant conditions, the system reduces the amount of detailed analysis required later, thereby decreasing overall analysis time while maintaining completeness.
Solution Approach 2:
The patent applies partial analysis by focusing root cause identification efforts on the most promising candidates within each group rather than exhaustively analyzing all abnormal operating conditions. This partial action approach, guided by the grouping structure, achieves sufficient completeness without the time cost of full exhaustive analysis.
3Reliability
If existing methods use heterogeneous monitoring agents and multiple data sources to monitor complex environments, then monitoring coverage and detection capability are improved, but the complexity of integrating and processing multi-source data increases
Solution Approach 1:
The patent creates a unified topology model that serves multiple functions: it represents the monitored environment structure, stores resource dependency information, and enables causal relationship analysis. This universal model consolidates multiple data sources (topology data, resource utilization data, transaction trace data) into a single integrated structure, reducing integration complexity while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The patent merges multiple types of monitoring data (topology-related data, resource utilization data, transaction trace data) into a unified topology model. This merging consolidates information from heterogeneous monitoring agents and multiple data sources into a single coherent representation, simplifying processing while preserving the reliability and coverage provided by multi-source monitoring.
Data Source
AI summary
A system and method for the aggregation and grouping of previously identified, causally related abnormal operating condition, that are observed in a monitored environment, is disclosed. Agents are deployed to the monitored environment which capture data describing structural aspects of the monitored environment, as well as data describing activities performed on it, like the execution of distributed transactions. The data describing structural aspects is aggregated into a topology model which describes individual components of the monitored environments, their communication activities and resource dependencies and which also identifies and groups components that serve the same purpose, like e.g. processes executing the same code. Activity related monitoring data is constantly monitored to identify abnormal operating conditions. Data describing abnormal operating condition is analyzed in combination with topology data to identify networks of causally related abnormal operating conditions. Causally related abnormal operating conditions are then grouped using known topological resource and same purpose dependencies. Identified groups are analyzed to determine their root cause relevance.