Engineering and Technology
| Open Access | Adaptive Resilience: Integrating Ansible-Based Dynamic Scaling and Formal Chaos Engineering for AI-Enabled Microservices in Hybrid Cloud Environments
Elena V. Rostova , Independent Researcher, Cloud Systems & Reliability Engineering, Zurich, Switzerland Marcus J. Thorne , Institute for Computational Resilience, Boston, MA, USAAbstract
Background: The proliferation of AI-enabled microservices in enterprise environments has necessitated robust strategies for dynamic scaling. While Platform-as-a-Service (PaaS) offerings provide inherent scalability, they often suffer from "cold-start" latency and unpredictable cost implications during varying workloads, such as refinery turnarounds or large-scale data processing events.
Methods: This study introduces a Resilient Scaling Orchestrator (RSO) that integrates Ansible-based automation with formal process algebraic models to optimize end-to-end dynamic scaling. We employ a hybrid methodology that combines theoretical formal component modeling to predict system states with practical chaos engineering experiments to validate resilience. The approach leverages Ansible playbooks to pre-warm instances based on predictive heuristics, mitigating cold-start latency.
Results: Experimental validation using industry-standard microservices benchmarks demonstrates that the proposed RSO reduces cold-start latency by approximately 40% compared to reactive Azure PaaS autoscaling. Furthermore, the integration of formal verification ensures that 99.9% of scaling operations maintain transactional integrity even under induced chaos scenarios.
Conclusion: The findings suggest that combining infrastructure-as-code tools with formal mathematical modeling provides a superior framework for managing the cost-performance trade-off in cloud-native AI applications.
Keywords
Cloud Computing, Dynamic Scaling, Microservices,, Ansible
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