Leveraging Serverless Framework Simulations for Cost-Efficient and Scalable Deployment in E-Learning Environments

Abstract

The rise of serverless computing presents a transformative opportunity for scaling and optimizing cloud-based e-learning platforms. This study explores the integration of the Serverless Framework to simulate, analyze, and enhance deployment strategies within educational ecosystems characterized by variable user loads and content access patterns. By leveraging the unit test architecture and plugin modules of the open-source Serverless Framework particularly those aligned with AWS Lambda and event-driven provisioning this research develops a simulated model of resource invocation, performance benchmarking, and cost estimation. Real-world usage data from leading e-learning platforms (Coursera, Udemy, edX, and Skillshare) is used to replicate typical platform behavior, while comparative metrics from virtual machine-based deployments inform baseline evaluations. Findings reveal that serverless deployments not only reduce operational costs through granular billing and auto-scaling but also improve resilience and responsiveness to peak demand in pedagogical workflows. The study concludes by proposing a hybrid adaptive deployment model tailored for educational content delivery, combining the predictability of reserved compute with the elasticity of serverless backends.