Single Physical Computer Research Articles

Priority-Based Virtual Machine Selection Algorithm in Cloud Computing

Cloud computing technology has gained the attention of researchers in recent years. Almost every application is using cloud computing in one way or another. Virtualization allows running many virtual machines on a single physical computer by sharing its resources. Users can store their data on datacenter and run their applications from anywhere using the internet and pay as per service level agreement documents accordingly. It leads to an increase in demand for cloud services and may decrease the quality of service. This paper presents a priority-based selection of virtual machines by cloud service provider. The virtual machines in the cloud datacenter are configured as Amazon EC2 and algorithm is simulated in cloud-sim simulator. The results justify that proposed priority-based virtual machine algorithm shortens the makespan, by 11.43 % and 5.81 %, average waiting time by 28.80 % and 24.50%, and cost of using the virtual machine by 21.24% and 11.54% as compared to FCFS and ACO respectively, hence improving quality of service.

OPTIMALISASI SUMBER DAYA KOMPUTER DENGAN VIRTUALISASI SERVER MENGGUNAKAN PROXMOX VE

Virtualization technology is a topic that began to be scrutinized by researchers. This is inseparable from the technology's ability to reduce the cost of providing infrastructure and operations independently for each service to be served. With server virtualization, we can make several computers into one, which used to be possible for server computers to have three physical computers that can be used as one for the three server computers which in fact the resources on a single physical server computer will not use all the resources in one single physical computer. The existence of proxmox, then all servers can be connected in a network together so that they are able to streamline time. In addition, Proxmox has a web console that can be used for various purposes in server virtualization and maintenance. In this study, the performance of server virtualization using ProxmoxVE will be examined. This study is intended to determine the load of memory usage, load the CPU when running all the servers in Proxmox, and examine the Throughput, PacketLoss, Delay and Jitter on the server.
 Keyword: Proxmox, Virtualization, Resources, Server, Operating System

InfoFlow: A Distributed Algorithm to Detect Communities According to the Map Equation

Formidably sized networks are becoming more and more common, including in social sciences, biology, neuroscience, and the technology space. Many network sizes are expected to challenge the storage capability of a single physical computer. Here, we take two approaches to handle big networks: first, we look at how big data technology and distributed computing is an exciting approach to big data storage and processing. Second, most networks can be partitioned or labeled into communities, clusters, or modules, thus capturing the crux of the network while reducing detailed information, through the class of algorithms known as community detection. In this paper, we combine these two approaches, developing a distributed community detection algorithm to handle big networks. In particular, the map equation provides a way to identify network communities according to the information flow between nodes, where InfoMap is a greedy algorithm that uses the map equation. We develop discrete mathematics to adapt InfoMap into a distributed computing framework and then further develop the mathematics for a greedy algorithm, InfoFlow, which has logarithmic time complexity, compared to the linear complexity in InfoMap. Benchmark results of graphs up to millions of nodes and hundreds of millions of edges confirm the time complexity improvement, while maintaining community accuracy. Thus, we develop a map equation based community detection algorithm suitable for big network data processing.

Using computer virtualization and software tools to implement a low cost laboratory for the teaching of storage area networks

ABSTRACTThis article presents a lab approach for teaching storage area networks (SANs). Physical SANs are complex and costly infrastructures that require significant space. Both the cost and size are serious difficulties in setting up labs for teaching SANs. The lab approach presented in this article solves both problems by using computer virtualization, as well as software tools to emulate storage systems, so that lab activities can be carried out in a single physical computer. The article discusses the selection of a virtualization platform suitable for the teaching of SANs. Using the selected platform (the Hyper‐V hypervisor), a set of learning modules is designed. These modules teach students fundamental concepts and skills about SAN architecture, configuration and operation. The article provides brief descriptions of the modules. Then, the configuration schema of the practice platform used to support the SAN lab is explained. This configuration schema is designed for the cohabitation of multiple hypervisors (required to support the SAN lab activities) and a client operating system in the same physical computer. Thus, the SAN lab can be taught in a general purpose computer laboratory and coexist with other courses in the same physical lab. The proposed approach has been successfully implemented and verified by teaching the SAN lab in the context of an information technology degree in 2013 and 2014. © 2015 Wiley Periodicals, Inc. Comput. Appl. Eng. Educ. Comput Appl Eng Educ 23:715–723, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21644

A Formal Method of CPU Resources Scheduling in the Cloud Computing Environment

In the virtualization based cloud computing environment, multiple computers are allowed to run as virtual machines (VM) in a single physical computer. Efficient scheduling of limited underlying resources has been a key issue. This paper presents a formal method of CPU resources scheduling (FRS). VMs are divided into three resources statuses according to resources requirements and their run information. FRS scheduling is formally scheduling CPU resources according to the resources statuses. The implementation of FRS is confined to the VMM layer, without VM dependency. The evaluation shows that idle CPU resources of VMs are be used by those VMs which need more CPU resources and the CPU resources overall utilization is improved.

Simulating Resilient Server Using XEN Virtualization

Abstract Since servers play a critical role in data processing and data transmission for serving many clients, failures in servers cause not only performance degradation of the server itself but also threats to all the computers connected to the server. Resilient servers that can self-recognize failures, self-repair failures and self-replace failed parts are required when computer systems and networks become such huge-scale as witnessed by data centers and the cloud computing. As a preliminary study, we report simulations restricting ourselves to demonstrate the self-repair network (SRN) model in a homogeneous environment realized by a virtualization technique. Simulations are conducted using native and hosted VMM on a single physical computer. Three scenarios: hang faulty, DoS attack and virus infection, are simulated. These simulations demonstrated how a server with a homogeneous environment (realized by the self-repair network model using the virtualization technique) can or cannot keep the resilience, and even suggested a possibility and necessity of using a self-reconfiguration model to create diversity.

가상 스위치 구현 및 비교에 관한 연구

ABSTRACT Nowadays, most Internet servers run on virtual a network which connects each virtual machine, to process large amounts of data on single physical computer. The virtual machines for their own purposes are interconnected to other virtual server systems with various types of operating systems. Each virtual server may share one physical computer resources and operates at a service seamlessly to each other. Sometimes even the virtual machines are implemented using a method that lease the physical IT resources without purchasing physical server. Therefore, a virtual switch is needed to improve the performance when a large number of virtual machines operate on physical server. In this study, the implementation and investigation was performed for the virtual switches, OpenvSwitch and DPDKvSwitch, in order to provide the necessary high-performance cloud services to creative business. 키워드 : 가상머신, 가상네트워크, 고속화 가상스위치, 클라우드 서비스 Key word : Virtual machine, virtual network, accelerated virtual switch, cloud serviceJournal of the Korea Institute of Information andCommunication Engineering

Centralized and decentralized lab approaches based on different virtualization models

Virtualization technology allows a single physical computer to run multiple isolated virtual machines concurrently. To find cost-effective ways of delivering remote labs, decentralized and centrali...