Autonomous Orchestration of Stateful Applications with Predictive Resource Provisioning in Kubernetes via Hybrid Bayesian Optimization and Reinforcement Learning

**Abstract:** This paper proposes a novel framework for autonomous orchestration of stateful applications within Kubernetes environments, addressing the limitations of existing scheduling and resource management systems. Our approach, termed “PROgressively Optimized Resource Allocation for StateFul applications (PROSA-K),” combines Bayesian Optimization for initial resource provisioning strategy identification with Reinforcement Learning (RL) for dynamic adaptation and anomaly mitigation. PROSA-K leverages real-time performance metrics, application-specific workload profiles, and predictive modeling to proactively allocate and scale resources, minimizing latency, maximizing throughput, and ensuring high availability for demanding stateful workloads. The system aims to achieve a 30-50% improvement in resource utilization and a 15-25% reduction in latency for key stateful applications compared to traditional Kubernetes scheduling mechanisms. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*