Optimizing E-Learning in Resource-Constrained Environments: A Scalable High-Performance Computing and Cloud-Based Model for Moodle Deployment

The adoption of Learning Management Systems (LMS) has accelerated rapidly, particularly following the COVID-19 pandemic, as higher education institutions seek scalable e-learning infrastructure. Open-source platforms like Moodle have become pivotal by offering affordable and customizable pedagogical environments. However, numerous institutions in resource-constrained settings face significant infrastructural and scalability bottlenecks. This study deployed a quantitative, comparative practitioner case study design to evaluate the impact of the performance of migrating Moodle LMS from a centralized High-Performance Computing (HPC) setup to a distributed cloud-based virtual machine (VM) architecture. Using purposive sampling of six (6) federal universities in Nigeria (University of Lagos, University of Benin, Federal University of Technology Owerri, Ahmadu Bello University, Federal University of Agriculture Makurdi and Abubakar Tafawa Balewa University Bauchi), system performance metrics were monitored continuously over a 3-month pre- and post-migration period using edge polling via UptimeRobot from August to October 2025. The results showed improvements in performance. The average latency decreased by 45% (from 420 ms to 230 ms; p < 0.001), average page load time reduced from 3.8 s to 2.1 s (p < 0.001), and the average error rate fell from 4.3% to 1.2% p < 0.001. System availability remained fairly constant at 99.12% pre-migration vs 99.34% post-migration. Furthermore, the distributed VM model provided institutional autonomy for essential system upgrades, including PHP 8.2 and Moodle 5.0. The study recommended adopting the distributed architecture model of Moodle deployment on HPC owing to ease in scalability, increase in reliability, and institutional autonomy. Future studies on optimization of Moodle LMS should include survey on UI (User Interface) and UX (User Experience).