REST Architecture Optimization in Cloud Computing Ecosystem to Support E-Learning Platform

This study will present an application design process in the style of Representational State Transfer (REST) architecture to support the E-Learning platform in the cloud computing ecosystem. An application optimization process will be presented to provide E-Learning applications for schools, faculties or universities that in most cases need manual deployment and require more time for server provisioning. This process is optimized by providing application solutions that can provide speed of provisioning. The core system used Kubernetes containerization technology to provide scalability of growing E-Learning tenants. Evaluation of the core system architecture uses the Architecture Trade-off Analysis Method (ATAM) to evaluate aspect of performance and scalability as quality attributes. From the experimental results, the process of making new tenants for schools requires an average time of around 173.4 seconds. This meets the expectations of the set time limit of 5 minutes. The results of stress tests for 250 concurrent users show that the system has availability above 98%.Thus,education stakeholders such as schools and universities, no longer need to provide expensive e-learning infrastructure in the form of hardware or manpower to deploy the e-learning application on premise. In the future, this solution will provide a scalable E-Learning system that can spread at scale on the cloud computing ecosystem and support a Software as a Service solution in educational technology.