AI-Enhanced Pod Scheduling: Optimizing MERN and MEAN Stack Performance in Kubernetes

Microservices architecture has become the foundation of modern application development, offering enhanced scalability, flexibility, and maintainability. This paper presents a comparative performance evaluation of two widely used JavaScript-based stacks, MERN (MongoDB, Express, React, Node.js) and MEAN (MongoDB, Express, Angular, Node.js), deployed as microservices applications in Minikube, a local Kubernetes environment. While both stacks share a common backend technology stack, they differ in their front-end frameworks—React in MERN and Angular in MEAN—which influences their performance, scalability, and suitability for different application types. This study not only compares the architectural differences between MERN and MEAN but also introduces machine learning (ML)-based microservice optimization to enhance service instance selection and resource allocation in Kubernetes. The optimization leverages RF (Random Forest) and XGBoost ML algorithms for dynamic intelligent POD selection and scheduling, ensuring dynamic routing of microservice requests based on real-time performance metrics. Key evaluation criteria include latency, response time, CPU and memory utilization, scalability, and fault tolerance, monitored using Prometheus and Istio. The results provide insights into the efficiency of each stack in handling microservices workloads, helping developers and system architects choose the most suitable stack based on performance demands, resource constraints, and deployment complexity. The paper concludes with recommendations for optimizing microservices-based applications in Kubernetes environments using ML-driven orchestration strategies.