Temporal Graph Root Cause Analysis for Hybrid Cloud Diagnostics
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Solution Overview
Problem
In hybrid cloud and multi-cloud environments, detecting, diagnosing, and fixing problems across distributed workloads is challenging due to complexity and the difficulty in seamlessly monitoring and managing interactions between various components.
Innovation Solution
A graph-based method for problem diagnosis and root cause analysis is implemented, which generates a temporal graph from operation data to detect anomalies, determine directional impacts, and identify potential causes, allowing for efficient error propagation inference and root cause ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used in hybrid cloud and multi-cloud environments, then implementation is simple, but the ability to detect and diagnose problems across distributed workloads is insufficient
Solution Approach 1:
The patent segments the complex monitoring task by creating a temporal graph that divides the distributed workload monitoring into discrete temporal snapshots and causal relationships. Each node in the graph represents a specific operational state at a given time, allowing complex multi-cloud environments to be analyzed through structured, time-based segmentation of events and their relationships.
Solution Approach 2:
The patent introduces a temporal graph as an intermediary structure between raw operational data and problem diagnosis. This graph serves as a mediator that organizes operational data from multiple clouds into a unified causal model, enabling systematic analysis of error propagation and root cause identification without requiring direct complex interactions between monitoring components.
2Reliability
If comprehensive monitoring of distributed workloads is implemented, then problem detection capability is improved, but the complexity of managing interactions between components increases
Solution Approach 1:
The patent applies dynamics by creating a temporal graph that evolves over time, capturing the dynamic nature of distributed workload interactions. The graph structure adapts to reflect changing operational states and causal relationships, allowing the system to manage complex interactions through a flexible, time-aware model rather than static monitoring configurations.
Solution Approach 2:
The patent adds the temporal dimension to workload monitoring by constructing graphs that incorporate time-based relationships between operational events. This dimensional transformation allows the system to analyze not only spatial relationships between components but also temporal sequences of events, providing deeper insight into error propagation patterns across distributed systems.
3Measurement precision
If graph-based temporal analysis is used, then root cause identification is improved, but data processing complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-processing operational data into a structured temporal graph format before analysis. This preprocessing step organizes raw data into meaningful temporal patterns and causal relationships, reducing the complexity of subsequent root cause analysis while maintaining high measurement precision through structured data representation.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with graph-based computational approaches. Instead of using conventional data analysis techniques, the system employs graph theory and temporal pattern recognition to identify root causes, substituting complex mechanical processing with more efficient computational graph algorithms.
Data Source
AI summary
A computer-implemented method, system, and non-transitory machine readable medium for a graph-based analysis for an Information Technology (IT) operations includes generating a temporal graph by extracting one or more of operation objects, relations and attributes from operation data of workloads distributed across a plurality of levels of the IT operation within a predetermined time window. Anomalies are detected from the extracted operation data and annotating corresponding objects in the graph. A directional impact between corresponding objects on the temporal graph is determined, and the temporal graph is refined based on the determined directional impact. Accessible paths in the temporal graph indicating error propagation are searched, and potential causes for the detected anomalies in the temporal graph are identified. A list of the potential causes of the anomalies is generated, and a root cause ranked for each of the corresponding objects in the temporal graph.