AI Chaos Engineering for Proactive Software Resilience Testing
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Solution Overview
Problem
Current resilience monitoring methods are reactive and fail to detect software issues until they impact functionality, leading to costly downtime and lack integration with the client ecosystem, and there is a need for proactive resilience engineering with intelligent chaos scenario selection and infrastructure telemetry correlation.
Innovation Solution
A method and system using an intelligent resilience engineering module to identify business transactions, simulate chaos scenarios, and correlate infrastructure telemetry data with SDLC digital assets, leveraging AI models to predict infrastructure hotspots and provide proactive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If reactive resilience monitoring methods are used, then system simplicity is maintained, but issue detection capability deteriorates (issues detected only after impacting functionality)
Solution Approach 1:
The system performs preliminary actions by proactively simulating chaos scenarios and injecting faults into the software application before actual failures occur in production. This allows the system to detect and address resilience issues beforehand, rather than waiting for them to impact functionality. The intelligent resilience engineering module schedules and executes these simulations during development and testing phases.
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring infrastructure telemetry data, comparing it against expected behavior from chaos simulations, and using this information to improve future simulations and alerting. The correlation between infrastructure telemetry and SDLC digital assets creates a feedback mechanism that enhances detection capability while maintaining manageable complexity through automated learning.
2Reliability
If chaos engineering is implemented without intelligent scenario selection, then resiliency testing coverage increases, but system complexity and difficulty of operation worsen
Solution Approach 1:
The intelligent resilience engineering module performs self-service by automatically selecting appropriate chaos scenarios based on the software application's characteristics, infrastructure configuration, and historical data. The system uses AI/ML models to autonomously determine which scenarios to simulate without requiring manual configuration or expert intervention, thereby reducing operational complexity while maintaining comprehensive testing coverage.
Solution Approach 2:
The system dynamically changes parameters of chaos scenarios based on analyzed data from the software application and infrastructure. The intelligent module adjusts scenario selection, intensity, and timing by analyzing multiple parameters including application architecture, deployment configuration, and historical failure patterns, enabling comprehensive testing without fixed complex configurations.
3Productivity
If infrastructure telemetry is not correlated with SDLC digital assets, then data processing simplicity is maintained, but diagnostic capability and issue resolution efficiency deteriorate
Solution Approach 1:
The system merges infrastructure telemetry data with SDLC digital assets by correlating logs, metrics, and traces from the running system with source code, requirements, and defect information from the development lifecycle. This integration creates a unified view that connects operational data with development context, enabling faster diagnosis and resolution while managing complexity through standardized correlation frameworks.
Data Source
AI summary
The invention provides an intelligent resiliency engineering module for investigating resiliency of a software application, wherein the module is configured to identify business transactions associated with the software application to determine observability scenarios and orchestrate chaos engineering in a target infrastructure. The intelligent resilience engineering module identifies chaos simulation scenarios and a target infrastructure for the software application, to inject the one or more chaos simulation scenarios to simulate and orchestrate chaos attacks and the business transaction in the target infrastructure. Based on the simulation and orchestration, a correlation module by leveraging Artificial Intelligence (AI) models, correlates infrastructure telemetry data with Software Development Lifecycle (SDLC) digital assets. A predictive insights module predicts target infrastructure hotspots based on the correlation, current behavior of the target infrastructure, and behavioral conditions inferred based on historical insights on similar chaos simulation scenarios.