Intelligent Orchestration for Cloud-Native Scalability: Integrating AI-Driven Deployment with Formal Microservice Verification

Abstract

Background: Modern cloud-native architectures rely heavily on microservices to achieve agility, yet managing the dynamic scaling of these services introduces significant challenges. Specifically, the trade-off between minimizing infrastructure costs and preventing cold-start latency remains a critical bottleneck, particularly in high-stakes environments like refinery turnarounds. Methods: This study introduces the Predictive-Formal Scaler (PFS), a novel orchestration framework that integrates AI-driven automation with formal verification techniques. We leverage Ansible-based dynamic scaling on Azure PaaS and enhance it with a machine learning model designed to predict traffic bursts. Furthermore, we apply principles of asynchronous session subtyping to formally verify deployment configurations, ensuring that rapid scaling actions do not violate service contracts. The system performance is evaluated using distributed processing scenarios involving Elasticsearch shard selection. Results: Experimental analysis demonstrates that the PFS framework reduces cold-start latency by approximately 28% compared to standard reactive autoscalers. Additionally, the integration of formal verification reduced deployment configuration errors to near-zero, while optimized shard selection improved query throughput by 15%. Cost analysis reveals that while the AI component adds computational overhead, the net reduction in wasted idle resources results in a 12% overall cost saving. Conclusion: The integration of intelligent, predictive orchestration with rigorous formal methods offers a robust solution for managing complex cloud workloads. This approach not only enhances performance and reliability but also provides a mathematically sound basis for automated deployment decisions in critical infrastructure.