Adaptive Cloud Orchestration: Mitigating Cold-Start Latency and Optimizing Cost-Performance Trade-offs in Kubernetes via Reinforcement Learning and Ansible Integration

Abstract

The rapid proliferation of microservices architectures has established Kubernetes as the de facto standard for container orchestration. However, efficient auto-scaling remains a persistent challenge, particularly when balancing strict Service Level Agreements (SLAs) against the operational expenditures of cloud infrastructure. Traditional rule-based scaling mechanisms, such as the Horizontal Pod Autoscaler (HPA), often exhibit reactive latency, leading to "cold-start" delays during traffic bursts and resource over-provisioning during idle periods. This paper proposes a novel, hybrid orchestration framework that integrates Deep Reinforcement Learning (DRL) with Ansible-based configuration management to optimize the dynamic scaling of Azure PaaS environments. By treating the scaling problem as a Markov Decision Process (MDP), we develop a Q-learning agent capable of predicting workload fluctuations and preemptively provisioning resources. Furthermore, we leverage Ansible playbooks to parallelize node initialization, significantly reducing the initialization time of transient Virtual Machines (VMs). Our experimental results demonstrate that this approach reduces cold-start latency by approximately 40% and operational costs by 22% compared to standard reactive scaling methods, offering a robust solution for mixed interactive and batch workloads in enterprise environments.