Self-Configuring Chaos Policies From Log-Driven Failure Grouping
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Solution Overview
Problem
Manual configuration of chaos policies in chaos engineering is prone to human biases and fallacies, leading to inefficient and costly chaos testing, especially in dynamically changing IT environments with complex, interconnected systems.
Innovation Solution
A self-configuring chaos engine that automatically generates and adjusts chaos policies based on real-time monitoring of system logs, identifying and grouping issues, and intelligently magnifying their impact to simulate future potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration of chaos policies is performed, then human bias and fallacies are introduced, but configuration time and cost increase
Solution Approach 1:
The system performs self-service by automatically generating chaos policies through machine learning models that analyze historical system data and failure patterns. The ML-driven engine autonomously configures chaos experiments without human intervention, eliminating manual configuration time while maintaining or improving reliability through data-driven insights that avoid human biases.
Solution Approach 2:
The patent replaces the mechanical process of manual policy configuration with an automated machine learning system. The ML model processes historical data, identifies failure patterns, and generates chaos policies algorithmically, substituting human cognitive processes with computational algorithms that are faster and free from human fallacies.
2Reliability
If manual configuration of chaos policies is performed, then human biases are reduced, but adaptability to changing environments decreases
Solution Approach 1:
The chaos policy generation system is dynamic and continuously adapts to changing environments by processing real-time system data and updating its models. The ML engine learns from new failure patterns and system changes, automatically adjusting chaos policies to match current system states, thereby maintaining both accuracy and adaptability simultaneously.
Solution Approach 2:
The system incorporates feedback loops where chaos experiment results and system performance data are continuously fed back into the ML model. This feedback mechanism enables the system to learn from actual system behavior, refine its understanding of failure modes, and adjust future chaos policies accordingly, ensuring both accuracy and adaptability to environmental changes.
3Reliability
If comprehensive chaos testing is performed across all system components, then system resilience is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of comprehensive chaos testing into manageable components by analyzing system architecture and identifying critical failure points. The ML model divides the system into logical units and generates targeted chaos policies for each segment, maintaining thorough testing coverage while reducing overall policy complexity through structured decomposition.
Solution Approach 2:
The patent applies local quality by generating customized chaos policies tailored to specific system components and their unique failure modes. Rather than applying uniform chaos testing across all components, the ML model analyzes local system characteristics and creates targeted policies that address specific resilience needs of different system segments, improving effectiveness while managing complexity.
Data Source
AI summary
A log stream, generated in a system for at least a first issue, is monitored, the log stream comprising information regarding operations of at least one component in operable communication with the system. The log stream is analyzed to identify and group the first issue together one or more other issues in a grouping, wherein the grouping comprises issues that are associated with at least a first chaos that can be injected into the system. Based on the first chaos, a chaos policy, usable to run at a chaos experiment on the system, is generated automatically, wherein the chaos policy is configured to inject the chaos into the system. The chaos policy can be automatically added to a set of chaos policies associated with a chaos engine in operable communication with the system, which chaos engine runs one or more chaos experiments on the system.