Chaos Controller Injecting Failures Across Microservice Stacks
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Solution Overview
Problem
Current chaos engineering tools primarily focus on domain-specific failures, impacting end-users and lacking comprehensive observability across complex networks with multiple administrative domains, making it difficult to validate security and policy compliance effectively.
Innovation Solution
A system and method that utilize a chaos controller to generate and execute chaos hypotheses across a technology stack, communicating with full-stack observability agents to inject failures and collect metrics, ensuring minimal impact on end-users and providing dynamic compliance validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain-specific chaos engineering tools are used to inject failures, then failure injection capability is improved, but comprehensive observability across complex networks with multiple administrative domains deteriorates
Solution Approach 1:
The patent implements a universal chaos engineering platform that can inject failures across multiple domains (network, host, application, database) and collect observability data from various administrative domains through a single integrated system. The controller can selectively target different layers and domains while maintaining a unified observability perspective, resolving the contradiction between specialized failure injection and comprehensive observability.
Solution Approach 2:
The patent introduces an intermediary observability collection mechanism that aggregates data from multiple administrative domains and failure injection points into a centralized view. This intermediary layer synthesizes complex multi-domain data into actionable metrics, enabling comprehensive observability without requiring direct management of each individual domain's complexity.
2Measurement precision
If comprehensive observability is implemented across the entire stack, then measurement capability is improved, but system complexity and difficulty of operation worsen
Solution Approach 1:
The patent segments the observability system into modular components: domain-specific chaos engineering tools for failure injection, distributed observability agents for data collection, and a centralized controller for coordination. This segmentation allows each component to operate independently with well-defined interfaces, maintaining comprehensive measurement coverage while simplifying operational complexity through modularity.
Solution Approach 2:
The patent implements feedback mechanisms where the controller receives observability data from distributed agents, analyzes the impact of injected failures, and adjusts subsequent failure injection strategies. This closed-loop feedback automates operational decisions, reducing manual intervention complexity while maintaining comprehensive observability across the entire stack.
3Reliability
If chaos engineering failures are injected to validate security policies, then compliance validation capability is improved, but user impact and system disruption worsen
Solution Approach 1:
The patent applies local quality by enabling selective failure injection at specific domains and layers based on the compliance validation requirements. Instead of injecting failures system-wide, the controller targets specific administrative domains or service layers where policy validation is needed, minimizing overall user impact while maintaining comprehensive compliance validation capability.
Solution Approach 2:
The patent implements preliminary action by allowing compliance validation failures to be injected during off-peak hours or in controlled test environments before affecting production traffic. The system can pre-validate security policies using synthetic failures that do not impact actual users, reserving real user impact scenarios for authorized testing windows only.
Data Source
AI summary
In one embodiment, a method includes generating a security policy and converting the security policy into a chaos hypothesis. The method also includes initiating execution of the chaos hypothesis across a plurality of microservices within a technology stack. The method further includes receiving metrics associated with the execution of the chaos hypothesis across the plurality of microservices within the technology stack.