Distributed Incident Prevention Using ML Root Cause Correlation
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Solution Overview
Problem
Existing IT management tools operate in silos, generating numerous superfluous alerts and requiring significant manual effort to understand and correct server production incidents, while failing to identify causal relationships among components, leading to inefficient incident management in complex computer systems.
Innovation Solution
A machine learning-driven system for automated incident prevention that performs unsupervised online event pattern extraction, holistic root cause analysis, and remediation recommendation, using adaptive learning algorithms to map incidents and root causes to automatic remediation actions, integrating with existing tools like ServiceNow for feedback enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing IT management tools are used to monitor system components, then monitoring data is provided for system elements, but numerous superfluous alerts are generated requiring manual analysis
Solution Approach 1:
The patent combines multiple individual component monitoring tools into a unified system that monitors distributed systems holistically. By merging network, server, application, and database monitoring into a single integrated platform, the system eliminates the siloed operation of existing tools and reduces duplicate or conflicting alerts through centralized analysis.
Solution Approach 2:
The patent introduces an intermediary layer (the distributed system monitoring system) that sits between the monitored components and the alert generation process. This intermediary analyzes data from multiple sources, correlates events across components, and filters out false positives before generating alerts, thereby reducing superfluous notifications.
2Difficulty of detecting and measuring
If existing tools analyze system anomalies within individual components, then component-level monitoring is achieved, but causal relationships among components are not identified
Solution Approach 1:
The patent creates a universal monitoring system that performs multiple functions simultaneously: it monitors individual components, tracks inter-component relationships, identifies causal chains, and provides holistic system views. This multi-functional approach allows the system to maintain component-level detail while also capturing system-wide causal relationships.
Solution Approach 2:
The patent adds a new dimension to component monitoring by incorporating temporal and relational dimensions. Instead of only monitoring component states in isolation, the system tracks how anomalies propagate across components over time, enabling the identification of causal relationships through multi-dimensional data analysis.
3Ease of operation
If manual effort is used to understand and correct server production incidents, then incident analysis is performed, but significant time is consumed causing business penalties
Solution Approach 1:
The patent implements self-service capabilities through automated root cause analysis and remediation recommendation engines. The system automatically analyzes incident data, identifies root causes, and suggests or executes remediation actions without requiring manual intervention, thereby dramatically reducing incident resolution time while maintaining thorough analysis.
Solution Approach 2:
The patent performs preliminary actions by continuously learning from historical incident data and pre-computing remediation strategies. When an incident occurs, the system has already prepared relevant analysis frameworks and remediation options, enabling rapid response without starting the analysis process from scratch.
4Object-generated harmful factors
If alert notice reduction capabilities are provided to alleviate alert fatigue, then fewer alerts are presented, but server production incidents still require significant manual effort
Solution Approach 1:
The patent implements feedback loops where the system continuously monitors incident outcomes and remediation effectiveness. This feedback mechanism enables the system to learn from past incidents, improve its automated response capabilities, and progressively reduce the need for manual intervention while maintaining effective incident management.
Data Source
AI summary
An unsupervised pattern extraction system and method for extracting incident and root cause patterns from various kinds of machine 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 system utilizing Natural Language Processing and machine learning techniques to extract incident and root cause information from received incident reports and other system data. The system consists of both real time data collection (104) and analytics functions (200). The previously reported incident data is used to discover and apply remediation techniques to utilize prior remediation efforts to automatically classify and correct incidents. The system may then annotate a remediation data file with the technique applied. The system will utilize prior known remediation techniques for identified categories to predict and prevent future issues.