Stochastic Workload Synthesis via Constrained Generative AI: Enhancing Resilience Diagnostics in Containerized Microservices

Source abstract

Chaos engineering has emerged as the standard methodology for probing resilience in containerized microservice deployments; however, handcrafting workload profiles and fault scenarios fails to explore the high-dimensional fault space that production systems inhabit. We propose SynthChaos, a constrained generative AI pipeline coupling prompt-engineered large language model (LLM) fault specification with stochastic variational sampling (TimeVAE) to produce diverse, semantically valid workload profiles. The pipeline is evaluated on a 12-service Kubernetes/Istio/Prometheus testbed across 960 total experiment runs (4 conditions × 4 fault categories × 3 services × 4 workload profiles × 5 replications). All comparisons use Mann-Whitney U tests with Bonferroni correction; effect sizes are Cliff's delta. SynthChaos achieves 23.4% higher coverage entropy than the handcrafted baseline (H = 3.96 vs. 3.21 bits; p < 0.001, δ = 0.71), reduces mean time to detect (MTTD) by 17.2% (29.9 vs. 36.1 s; p < 0.001, δ = 0.68), and improves anomaly detection F1 from 0.785 to 0.836 (p = 0.003, δ = 0.42). Critically, unconstrained LLM generation degrades all metrics below the handcrafted baseline, confirming that constraint mechanisms — not stochastic diversity alone — drive performance gains. Scenario usability reaches 97.6% with only 2.5% generation overhead.