Multimodal Root Cause Analysis for Cloud Fault Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud systems face challenges in identifying vulnerabilities due to their immense scale, requiring significant time and resources to detect and prevent issues, and existing root cause analysis methods often rely on single-modal data, failing to capture long-term dependencies and correlations among multiple factors.
Innovation Solution
The method employs dilated convolutional neural networks to determine long-term temporal dependencies and causal relations from multi-modality data, using a fused causal graph to analyze correlations and perform autonomous system maintenance, incorporating contrastive representation learning to identify root causes and correct system faults.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-modal data analysis methods are used for root cause analysis, then the analysis process is simpler, but the ability to capture long-term dependencies and correlations among multiple factors is insufficient
Solution Approach 1:
The patent combines multiple data modalities (logs, metrics, traces) into a unified multi-modal analysis framework. The system integrates heterogeneous data sources with different characteristics and time scales, merging them into a comprehensive causal graph that captures relationships across all modalities, thereby improving root cause identification accuracy while managing complexity through unified processing
Solution Approach 2:
The patent introduces temporal dimension by using dilated convolutional neural networks to capture long-term dependencies across different time scales. This transforms the analysis from static single-point inspection to dynamic multi-temporal analysis, enabling the system to identify causal relationships that evolve over time and are not apparent in short-term snapshots
2Reliability
If manual vulnerability identification methods are used in cloud systems, then the process requires human expertise, but it consumes significant time and resources due to the immense scale of cloud systems
Solution Approach 1:
The system implements autonomous self-diagnosis capabilities through automated causal graph construction and root cause identification algorithms. The system automatically collects multi-modal data, constructs causal relationships, identifies root causes, and generates remediation recommendations without human intervention, enabling cloud systems to self-diagnose and self-heal, thereby reducing both time loss and resource consumption while maintaining high reliability
Solution Approach 2:
The patent implements continuous feedback loops where system performance data is continuously collected, analyzed, and used to update the causal graph and improve future root cause identification. The system learns from historical fault patterns and continuously refines its analysis capabilities, creating a self-improving system that becomes more reliable over time while maintaining efficient automated operation
3Measurement precision
If comprehensive multi-modality data is collected for root cause analysis, then the analysis coverage is improved, but the computational resources and processing time required increase significantly
Solution Approach 1:
The patent segments the comprehensive multi-modal data processing into distinct modular components: log parsing module, metric collection module, trace analysis module, and causal graph construction module. Each module processes specific data types independently using optimized algorithms, then integrates results. This segmentation enables parallel processing and reduces overall computational overhead while maintaining comprehensive analysis coverage and high detection accuracy
Data Source
AI summary
Systems and methods for online multi-modality root cause analysis. A root cause of a detected system fault can be identified based on a fused causal graph that represents the relationship of the factors and correlation of multi-modality data by, determining long-term temporal dependencies and causal relation from system entities and key performance indicators (KPI) of a cloud computing system using dilated convolutional neural networks, analyzing a correlation of factors from multi-modality data to assess contributions of the factors to causing a detected system fault, and learning a relationship of the factors and correlation of multi-modality data with contrastive representation learning. System maintenance that corrects the detected system fault caused by the root cause can be performed autonomously.