🤖 AI Summary
Current chaos engineering (CE) relies heavily on manual experiment design and result analysis, incurring high labor costs and steep expertise barriers. This paper proposes ChaosEater—the first automated framework systematically integrating large language model (LLM)-based agent workflows across the full CE lifecycle: elasticity requirement interpretation, experiment plan generation, Kubernetes-native fault injection, automated resilience testing, and actionable recovery recommendations. Its key contributions are: (1) the first end-to-end LLM-driven CE闭环—from requirement specification to repair strategy—fully eliminating the need for domain experts; and (2) a novel architecture combining hierarchical task decomposition, domain-knowledge augmentation, and multi-source validation, empirically validated across heterogeneous Kubernetes clusters of varying scales. Experimental results demonstrate significant reductions in both human effort and operational cost. Generated experiment plans and remediation suggestions are rigorously evaluated by both human engineers and independent LLMs, confirming their practicality, correctness, and domain alignment.
📝 Abstract
Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.