A new framework for deploying virtual desktop infrastructure using containerization approach

Abstract

The COVID-19 pandemic accelerated the shift to virtual learning, pushing educational institutions to adopt innovative solutions like Virtual Desktop Infrastructure (VDI). However, traditional VDI faces performance issues due to resource limitations. This research aims to enhance VDI performance and scalability by proposing a new framework based on containerization to address performance degradation issues such as unutilized CPU and memory usage in VDI. Four key performance metrics—CPU performance, memory performance, boot time, and latency—were evaluated. This research employed a qualitative method involving five phases: problem identification, planning, design and development, testing, and result analysis. The results show that the proposed containerization framework offers superior performance, with the proposed container standing out as the most efficient platform. For instance, the average boot time is merely 10 seconds compared to Windows' 39 seconds. Regarding RAM usage, the proposed container uses 7%, compared to 27% for Windows and 33% for Linux. This research highlights the potential of the proposed framework to revolutionize virtual learning by optimizing resource utilization, enabling educational institutions to support larger student populations with high-performance standards. It contributes to the ongoing efforts to optimize IT infrastructure for virtual learning, addressing key performance challenges.

Keywords

• cloud computing
• online learning and framework
• virtual desktop infrastructure(vdi)
• virtual machines containerization