Microservice Chaos Testing Prioritization Using API Accessibility Weights
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Solution Overview
Problem
Chaos testing in microservices environments is challenging due to the difficulty in selecting the most critical microservices for testing, which can be time-consuming and resource-intensive, and existing methods fail to account for the dynamic and hidden significance of application programming interfaces (APIs) in determining system resilience.
Innovation Solution
A system is implemented to assign accessibility weights (AWs) to APIs based on their importance and usage patterns, inferring weights for unseen APIs, and using total aggregated weights (TAWs) to prioritize microservices for chaos testing, ensuring critical services are identified for testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chaos testing is performed on all microservices, then system resilience is improved, but resource consumption and time cost increase significantly
Solution Approach 1:
The patent segments the microservice portfolio by computing accessibility weights for individual services based on their exposure to external traffic. This segmentation identifies a critical subset of microservices that handle the majority of accessibility demands, allowing chaos testing resources to be concentrated on these high-impact services rather than uniformly testing all services, thus resolving the contradiction between comprehensive resilience and resource efficiency.
Solution Approach 2:
The patent applies local quality by differentiating testing intensity based on the specific characteristics of each microservice. Services with higher accessibility weights receive prioritized testing attention, while services with lower weights receive reduced testing resources. This non-uniform distribution of testing quality matches the actual impact each service has on system accessibility, optimizing the resilience-resource tradeoff.
2Reliability
If chaos testing prioritization is performed manually, then testing coverage is comprehensive, but time consumption increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically compute accessibility weights and generate prioritization rankings without manual intervention. The system autonomously analyzes microservice architecture, calculates accessibility metrics, and produces a ready-to-execute testing priority list, eliminating the time-consuming manual assessment process while maintaining comprehensive testing coverage through data-driven insights.
Solution Approach 2:
The patent transforms the qualitative judgment of service importance into quantitative accessibility weight parameters. By computing these parameters automatically from system architecture and traffic patterns, the patent converts manual prioritization into an automated parameter-based ranking system, significantly reducing time consumption while preserving thorough testing coverage through objective metric evaluation.
3Ease of manufacture
If existing chaos testing methods are used, then implementation is simple, but dynamic and hidden significance of APIs is not accounted for
Solution Approach 1:
The patent introduces accessibility weight as an intermediary metric that bridges the gap between simple implementation and precise API significance assessment. This intermediary parameter aggregates multiple factors including direct API accessibility, transitive accessibility through service chains, and invocation frequency, providing a nuanced measure of API importance that enhances measurement precision while maintaining implementation feasibility through systematic computation.
Solution Approach 2:
The patent adds another dimension to API significance assessment by considering not only direct accessibility but also transitive accessibility through service dependencies and invocation patterns. This multi-dimensional approach captures the hidden significance of APIs that may not be directly exposed but play critical roles in service chains, thereby improving measurement precision without overly complicating the implementation through systematic weight aggregation.
Data Source
AI summary
A system can identify respective first accessibility weights associated with at least some application programming interfaces (APIs) of respective APIs exposed by respective microservices of a group of microservices of a microservice architecture. The system can determine a second accessibility weight for an API of the respective APIs based on how often the API is invoked with at least a subset of the respective APIs, and based on second respective accessibility weights of the respective first accessibility weights that are associated with at least the subset of the respective APIs. The system can determine respective total accessibility weights for the respective microservices based on the first accessibility weights and the second accessibility weight. The system can, based on the respective total accessibility weights, determine a selected microservice of the group of microservices on which to perform chaos testing. The system can perform the chaos testing on the selected microservice.