Unsupervised Event Pattern Extraction for Distributed System Root Cause Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Complex computer systems face challenges in detecting and analyzing system anomalies across inter-dependent components, as existing solutions focus on individual components and require manual extraction of insights from vast amounts of raw data, failing to identify causal relationships that can lead to system-wide failures.
Innovation Solution
An unsupervised online event pattern extraction and holistic root cause analysis system using adaptive pattern learning algorithms, statistical classification methods, and machine learning to identify key features and patterns in metric and log data, along with system call traces, to automatically detect anomalies and predict cascading failures, enabling automated remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing solutions focus on detecting anomalous metric values within individual components, then anomaly detection capability is improved, but the ability to identify causal relationships and predict system-wide failures deteriorates
Solution Approach 1:
The patent merges individual component anomaly detection with system-wide causal relationship analysis by integrating correlation analysis modules that link anomalies across multiple components. The system combines metric value detection with log data analysis and causal relationship inference to achieve holistic root cause analysis, resolving the contradiction between focused detection and system-wide understanding.
Solution Approach 2:
The patent adds temporal and causal dimensions to traditional metric-based anomaly detection. By analyzing time-series data across multiple components and inferring causal relationships, the system transitions from static metric monitoring to dynamic, context-aware anomaly analysis that predicts system-wide failures before they occur.
2Ease of operation
If manual extraction of insights is performed from vast amounts of raw data, then data processing flexibility is maintained, but processing time and labor requirements increase significantly
Solution Approach 1:
The patent implements self-service through automated pattern learning algorithms that automatically extract insights from raw data without manual intervention. The system performs unsupervised learning to identify anomaly patterns, correlates events across components, and generates root cause analyses automatically, eliminating the need for manual data processing while maintaining operational flexibility through configurable alerting and notification mechanisms.
3Extent of automation
If unsupervised online event pattern extraction is implemented, then automated anomaly detection is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on extracting and analyzing only the most relevant patterns from vast amounts of data. The unsupervised learning algorithms identify and prioritize anomaly patterns based on their statistical significance and impact, rather than processing all data uniformly, thus reducing computational overhead while maintaining high automation capability.
Data Source
AI summary
An unsupervised pattern extraction system and method for extracting user interested patterns from various kinds of data such as system-level metric values, system call traces, and semi-structured or free form text log data and performing holistic root cause analysis for distributed systems. The distributed system includes a plurality of computer machines or smart devices. The system consists of both real time data collection and analytics functions. The analytics functions automatically extract event patterns and recognize recurrent events in real time by analyzing collected data streams from different sources. A root cause analysis component analyzes the extracted events and identifies both correlation and causality relationships among different components to pinpoint root cause of a networked-system anomaly. Furthermore, an anomaly impact prediction component estimates the impact scope of the detected anomaly and raises early alarms about impending service outages or application performance degradations based on the identified correlation and causality relationships.