AI Microservice Failure Model Agent
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Solution Overview
Problem
Distributed software systems face unpredictable outcomes due to interactions between services, compounded by rare but disruptive events, making it challenging for developers to identify and address systemic weaknesses before they cause system-wide issues.
Innovation Solution
An artificial intelligence (AI) failure model agent that analyzes microservice executable code to build failure models, recommending or automatically running targeted scripts to simulate specific conditions, thereby reducing the complexity of testing and improving resilience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive testing of distributed systems is performed to identify systemic weaknesses, then system reliability is improved, but testing complexity and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing static code analysis and building failure models before actual system testing. The system analyzes microservice code, identifies potential failure points, and creates failure injection models in advance, so that when testing occurs, only targeted failure scenarios need to be injected rather than进行全面 comprehensive testing. This reduces testing complexity while maintaining reliability improvement.
Solution Approach 2:
The patent uses copying by creating failure models that replicate real-world failure scenarios in a controlled manner. Instead of testing all possible system failures directly, the system creates simplified models of failure conditions (such as service unavailability, timeout scenarios, circuit breaker states) that can be injected and observed. This allows comprehensive reliability testing without the full complexity of testing every possible real-world failure mode.
2Productivity
If targeted failure injection scripts are used to simulate specific failure conditions, then testing efficiency is improved, but the number of required test scripts increases
Solution Approach 1:
The patent applies universality by designing a unified failure injection framework that can handle multiple types of failures through a common interface. The system provides universal failure models that can be applied across different microservices and failure scenarios (service unavailable, timeout, circuit breaker, etc.). This single universal framework replaces the need for numerous separate, specialized test scripts, improving efficiency while reducing the total quantity of testing artifacts needed.
3Measurement precision
If static code analysis is performed to build failure models, then failure scenario identification is improved, but analysis time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing static code analysis selectively on critical portions of the microservice code rather than analyzing every line comprehensively. The system focuses on identifying failure-relevant code patterns (such as service calls, timeout configurations, circuit breaker logic) while skipping less critical code sections. This partial analysis approach maintains sufficient precision for failure scenario identification while significantly reducing analysis time and computational resource requirements.
Data Source
AI summary
Generally described, one or more aspects of the present application correspond to an artificial intelligence (AI) system that helps developers build failure-resilient microservices. The AI system can analyze the code of a microservice to identify other, similar microservices, and then use historical data about performance of those similar microservices to build a failure model for the microservice. The AI system can also automatically run failure tests based on the failure model, and can suggest code changes (based on analyzing historical code changes and test performance data) that can help a microservice become resilient to a particular failure.