<![CDATA[Cloud technology offers significant advantages; however, its high implementation costs and high hardware requirements pose barriers to small-scale deployments and educational institutions. This study addresses these challenges by investigating the performance of OpenStack deployed via DevStack on a single-node server equipped with an Intel Core i7 processor, 16 GB of RAM, and a 500 GB solid-state drive (SSD) under resource-constrained conditions. We implemented a resource tuning approach by turning off non-essential services (including Cinder, Heat, and Tempest) and adjusting Nova's memory configurations to minimize overhead. Real-time system monitoring was performed using Prometheus and Grafana to examine trends in CPU, memory, and swap utilization across three configurations: default, optimized (RAM=1024 MB), and minimalist (RAM=512 MB). Our empirical results show that the optimized setup enhances system efficiency, decreasing CPU use and memory usage from 86% to 70.90% while maintaining the ability to run up to ten virtual machines with varying operating systems (e.g., CirrOS, Ubuntu 24.04 Server LTS). However, the minimalist configurations, which aim for aggressive swap utilization and reach 100% swap saturation when running 8 VMs under idle workloads, consequently compromise overall system responsiveness despite lower CPU usage. Efficiency in this context is defined as conserving RAM and CPU usage without degrading basic system responsiveness. This highlights a critical trade-off between RAM conservation and overall system responsiveness. This research provides practical insights into designing cost-effective and lightweight OpenStack environments. It establishes a crucial threshold for memory optimization, preventing performance degradation caused by excessive swap usage, particularly in resource-constrained research settings.]]>
