Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering

Abstract

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim simple topological models provide fast, low-risk availability estimates. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests)—providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median signed error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results suggest a hybrid workflow: using models to monitor resilience in real-time and filter scenarios, while reserving expensive live validation for critical risks.

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