Introduction
Can you imagine the modern world without visualization and cloud computing? If you answer yes, you might not realize how permeated the modern world is with technology or how many fields rely on technology to achieve their goals. In particular, many instruments are utilized to facilitate learning and skill development in different stages of an educational journey. One such instrument is a virtual training lab (VTL), which allows students to hone their laboratory skills in a virtual environment. Let’s examine in more detail how VLTs and cloud computing are linked.
VTL, Visualization, and Cloud Computing
In their 2015 article, A High-Performance Cloud Computing Solution for Training and Laboratories, Jarraya and Khedher (2015) briefly discuss the use of cloud computing in VLTs. The authors also propose a solution to improve educational institutions’ instructional and research requirements. But first, let’s consider how VLTs work and why there is a need for an efficient cloud server solution.
First, VLTs use an enormous number of images that are stored on servers. Second, their processing should be instantaneous to meet productivity and performance demands. Investing in a piece of technology that is less efficient and reliable than suitable old beakers is a waste of money for most institutions.
Therefore, Jarraya and Khedher (2015) proposed a cloud computing solution that can keep up with the expansion demands while maintaining efficiency. In simple terms, server clusters or a complex hardware-middleware-software chain connecting students and virtual environments to allocate user resources properly are deployed (Jarraya & Khedher, 2015).
Thus, by raising the computational complexity demand for resources and reducing the response time, cloud computing enhanced their service level agreements (Jarraya & Khedher, 2015). But there is a catch, as along with these benefits, significant requirements are imposed in terms of architecture and optimization
VLT Architecture and Optimization
The architecture of a piece of technology is just as important as the architecture of a building. Without a thorough design, both will collapse on those using it. Architecture and optimization were, of course, addressed by Jarraya and Khedher (2015). They used the trick to view the workload as a standalone service or group of executable programs.
Next, as with the selection of appropriate materials for building, software that runs virtual machines, or hypervisors, is selected. In this case, VMware ESXi and Microsoft Hyper-V R2 were used, with the former edging the former out (Jarraya & Khedher, 2015). The following step ensures hypervisors “intelligently manage resource allocation” when operating on a server (Jarraya & Khedher, 2015, p. 2). Thus, the software offers consistent service levels with realistic workloads.
However, we should not discount the fact that the cloud design imposes several design restrictions. As Jarraya and Khedher (2015) focused on private cloud design, predictably, they had to adhere to a specific scale unit pattern. A scale unit is a collection of computing, storage, and networking resources deployed individually or in groups that support self-management without requiring it to be physically reconfigured.
The architecture of the VTL is visually represented in Figure 1 (Jarraya & Khedher, 2015). In their research, with the help of the gathered evidence, the authors concluded that conducting a single session to load all the modules and run commands, as shown in Figure 2, is more efficient (Jarraya & Khedher, 2015). In addition, the approach demonstrated in Figure 3 is also viable (Jarraya & Khedher, 2015).
Concluding Thoughts
Cloud computing is here to stay, and its educational benefits are undeniable. The article at issue is a reliable piece of research highlighting the crucial aspects of cloud computing solutions, such as architecture and optimization. It considers different approaches to scaling and implementing two popular hypervisors: VMware ESXi and Microsoft Hyper-V R2.
However, the experimental architecture, albeit providing faster resource access, might showcase different results in scenarios outside VLT, which comes at the expense of research validity. Even though it is somewhat outdated by modern standards, it offers one valuable idea to stand the test of ages: the architecture of a VLT is critical to its scalability and performance.
Reference
Jarraya, M., & Khedher, O. (2015). A high performance cloud computing solution for training and laboratories. In 2015 International Conference on Cloud Computing (ICCC) (pp. 1–4). IEEE. Web.