Orchestrating Elasticity: A Comparative Analysis Of AI-Driven Predictive Scaling Versus Reactive Auto-Scaling In Microservices Architectures

Abstract

As cloud computing paradigms shift towards microservices and containerized architectures, the efficiency of resource allocation remains a critical challenge. Traditional reactive auto-scaling mechanisms, which rely on threshold-based metrics such as CPU and memory utilization, often fail to address sudden workload spikes, leading to service degradation and "cold start" latency. This study presents a comparative analysis between standard reactive scaling, Ansible-based dynamic scaling on Azure PaaS, and a novel AI-driven predictive scaling framework. Drawing on recent developments in Artificial Intelligence and Infrastructure as Code (IaC), we evaluate these approaches using a synthesized workload representative of complex industrial scenarios, such as refinery turnarounds, and high-velocity e-commerce transactions. Our methodology involves the deployment of a Long Short-Term Memory (LSTM) neural network to forecast workload demands 10 minutes in advance, triggering proactive scaling actions. We contrast this with standard Kubernetes Horizontal Pod Autoscaling (HPA) and rule-based Ansible automation. The results demonstrate that the AI-driven predictive model reduces 95th percentile latency by approximately 34% compared to reactive approaches and mitigates cold-start latency by 90%. Furthermore, while the predictive model incurs a marginal computational overhead, it reduces overall cloud expenditure by 18% by minimizing over-provisioning during idle periods. The findings suggest that integrating AI into the orchestration layer is essential for the next generation of cost-efficient, high-performance cloud architectures.