Evaluating Asynchronous Semantics in Trace-Discovered Resilience Models: A Case Study on the OpenTelemetry Demo

• URL: http://arxiv.org/abs/2512.12314v1
• Date: Sat, 13 Dec 2025 13:08:05 GMT
• Title: Evaluating Asynchronous Semantics in Trace-Discovered Resilience Models: A Case Study on the OpenTelemetry Demo
• Abstract summary: We revisit a trace-discovered connectivity model that derives a service dependency graph from traces.<n>We use Monte Carlo simulation to estimate endpoint availability under fail-stop service failures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While distributed tracing and chaos engineering are becoming standard for microservices, resilience models remain largely manual and bespoke. We revisit a trace-discovered connectivity model that derives a service dependency graph from traces and uses Monte Carlo simulation to estimate endpoint availability under fail-stop service failures. Compared to earlier work, we (i) derive the graph directly from raw OpenTelemetry traces, (ii) attach endpoint-specific success predicates, and (iii) add a simple asynchronous semantics that treats Kafka edges as non-blocking for immediate HTTP success. We apply this model to the OpenTelemetry Demo ("Astronomy Shop") using a GitHub Actions workflow that discovers the graph, runs simulations, and executes chaos experiments that randomly kill microservices in a Docker Compose deployment. Across the studied failure fractions, the model reproduces the overall availability degradation curve, while asynchronous semantics for Kafka edges change predicted availabilities by at most about 10^(-5) (0.001 percentage points). This null result suggests that for immediate HTTP availability in this case study, explicitly modeling asynchronous dependencies is not warranted, and a simpler connectivity-only model is sufficient.

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