Using Load Balancing on Google Cloud Platform to Build an Online Learning Platform

Abstract

In the digital era, the need for a reliable and scalable online learning system is increasing, especially for educational institutions. This research aims to implement load balancing on Google Cloud Platform (GCP) to build an efficient and stable Moodle-based online learning platform. The system is developed by utilizing GCP services such as Compute Engine, Cloud SQL, Cloud Memorystore Redis, and Cloud Filestore, and supported by HTTPS Load Balancer to ensure optimal load distribution between servers. In addition, the Autoscaler feature is enabled so that the system can automatically adjust capacity according to changes in workload. The test results show that the system is able to handle spikes in the number of users well, keep the load distribution balanced, and ensure stable response time. The autoscaler plays an important role in optimizing resource utilization, allowing applications to continue performing optimally despite significant increases in user activity. This implementation proves that load balancing on GCP can improve the performance, scalability and reliability of the online learning platform, while providing a better user experience. This research also provides guidance for educational institutions in adopting cloud technology to effectively support online learning systems.