Deploying an Ad-Hoc Computing Cluster Overlaid on Top of Public Desktops

A computer laboratory is often a homogeneous environment, in which the computers have the same hardware and software settings. Conducting system tests in this laboratory environment is quite challenging, as the laboratory is supposed to be shared with regular classes. This manuscript details the use of desktop virtualization to deploy dynamically a virtual cluster for testing and ad-hoc purposes. The virtual cluster can support an environment completely different from the physical environment and provide application isolation essential for separating the testing environment from the regular class activities. Windows 7 OS was running in the host desktops, and VMware Workstation was employed as the desktop virtualization manager. The deployed virtual cluster comprised virtual desktops installed with Ubuntu Desktop Linux OS. Lightweight applications using VMware VIX library and shell scripts were developed and employed to manage job submission to the virtual cluster. Evaluations on the virtual cluster’s deployment show that we can leverage on desktop virtualization to quickly and dynamically deploy a testing environment while exploiting the underutilized compute resources

Implementation of Private Cloud for Optimization of Computer Resources at University

The development of computer technology and applications are very rapid, causing the needs of computer use in the organization has increased. The performance of personal computers has increased rapidly, but it cannot be utilized properly. Therefore, personal computers can be used together to perform parallel processing, so that data processing time can be shortened. Private cloud, as an alternative solution to provide cloud services to organizations that are on the same network. The service provided is Infrastructure as a Service (IaaS), where users can request certain types of services and use certain operating systems. Universities have many computers in the laboratory that have considerable resources if they are able to optimize the use of each computer as a processing resource. Supported by high specification on each computer, it is suitable for private cloud implementation. Using the Openstack framework can produce a good enough performance to perform parallel data processing. The test results show that the parallel processing performance works as expected

Computer Science Laboratory Environment Using Docker

Docker is an application that is based on open source technology that allows developers or anyone to create, run, do experiments and launch application in a container. Docker make the process of packaging application components together quickly in an insolated container, so it can run in local infrastructure without changing configuration on the container. Docker is also very light and fast when compared to hypervisor-based virtual machine. A university has a considerable number of students, while the number of computers in the department is not as many as the students. It makes the students not comfortable working on one computer, because configuring a computer on every lesson is different. Therefore, implementation of Docker application carried out in the laboratory. Docker and RAID storage system were implemented, testing was done by measuring the speed of Docker image transfer, and its effectiveness for laboratory programs

Optimization of Computer Resources Using OpenStack Private Cloud

The rapid development of computer technology and applications has caused the increasing need for computer use in an organization. The performance of personal computers has increased rapidly, but they cannot be utilized properly. Therefore, personal computers can be used together to perform parallel processing, in order to shorten data processing time. This paper discusses Private cloud, which could be utilized as an alternative solution to provide cloud services to organizations that are in the same network. The service provided was Infrastructure as a Service (IaaS), where users can request certain types of services and use certain operating systems. Most universities have many computers in the laboratory that have considerable resources for a superb processing potential if they optimized properly. Supported by high specification on each computer, the laboratory is suitable for private cloud implementation. The use of the OpenStack framework can produce sufficient performance to perform parallel data processing. The test results showed that the parallel processing performance worked as expected

Optimization of Computer Resources Using OpenStack Private Cloud

The rapid development of computer technology and applications has caused the increasing need for computer use in an organization. The performance of personal computers has increased rapidly, but they cannot be utilized properly. Therefore, personal computers can be used together to perform parallel processing, in order to shorten data processing time. This paper discusses Private cloud, which could be utilized as an alternative solution to provide cloud services to organizations that are in the same network. The service provided was Infrastructure as a Service (IaaS), where users can request certain types of services and use certain operating systems. Most universities have many computers in the laboratory that have considerable resources for a superb processing potential if they optimized properly. Supported by high specification on each computer, the laboratory is suitable for private cloud implementation. The use of the OpenStack framework can produce sufficient performance to perform parallel data processing. The test results showed that the parallel processing performance worked as expected

Private Cloud Deployment on Shared Computer Labs

A computer laboratory in a school or college is often shared for multiple class and lab sessions. However, often the computers in the lab are just left idling for an extended period of time. Those are potential resources to be harvested for cloud services. This manuscript details the deployment of a private cloud on the shared computer labs. Fundamental services like operation manager, configuration manager, cloud manager, and schedule manager were put up to power on/off computers remotely, specify each computer’s OS configuration, manage cloud services (i.e., provision and retire virtual machines), and schedule OS switching tasks, respectively. OpenStack was employed to manage computer resources for cloud services. The deployment of private cloud can improve the computers’ utilization on the shared computer labs

Server Scalability Using Kubernetes

An enterprise that has implemented virtualization can consolidate multiple servers into fewer host servers and get the benefits of reduced space, power, and administrative requirements. Sharing their hosts’ operating system resources, containerization significantly reduces workloads, and is known as a lightweight virtualization. Kubernetes is commonly used to automatically deploy and scale application containers. The scalability of these application containers can be applied to Kubernetes with several supporting parameters. It is expected that the exploitation of scalability will improve performance and server response time to users without reducing server utility capabilities. This research focuses on applying the scalability in Kubernetes and evaluating its performance on overcoming the increasing number of concurrent users accessing academic data. This research employed 3 computers: one computer as the master node and two others as worker nodes. Simulations are performed by an application that generates multiple user behaviors accessing various microservice URLs. Two scenarios were designed to evaluate the CPU load on single and multiple servers. On multiple servers, the server scalability was enabled to serve the user requests. Implementation of scalability to the containers (on multiple servers) reduces the CPU usage pod due to the distribution of loads to containers that are scattered in many workers. Besides CPU load, this research also measured the server’s response time in responding user requests. Response time on multiple servers takes longer time than that on single server due to the overhead delay of scaling container

Streaming Media Implementation in Moodle-Based E-Leaming Application

Teaching and learning processes develop over time, and the technology behind it are growing too fast, which makes the process of learning with online media (or often referred to as e-learning). This phenomenon is interesting to examine because some e-learning systems that exist today are not yet fully secure, in the sense that it is still easy to disseminate without any copyright and they are not used wisely. The purpose of this research is to produce an application that can be used as a media for e-learning of streaming audio and video without storing the file on local cache device so that the file residing in e-learning media cannot disseminated. As a research methodology using deductive qualitative method, data collective process done by literature study, interview and observation. After the data collected, the result is e-learning software based on website and mobile that can be used to stream media files without downloaded. The result of this research and analysis shows that streaming with RTSP protocol can be used in e-learning software with a limited number of users