15,625 research outputs found
Grid-enabling FIRST: Speeding up simulation applications using WinGrid
The vision of grid computing is to make computational power, storage capacity, data and applications available to users as readily as electricity and other utilities. Grid infrastructures and applications have traditionally been geared towards dedicated, centralized, high performance clusters running on UNIX flavour operating systems (commonly referred to as cluster-based grid computing). This can be contrasted with desktop-based grid computing which refers to the aggregation of non-dedicated, de-centralized, commodity PCs connected through a network and running (mostly) the Microsoft Windowstrade operating system. Large scale adoption of such Windowstrade-based grid infrastructure may be facilitated via grid-enabling existing Windows applications. This paper presents the WinGridtrade approach to grid enabling existing Windowstrade based commercial-off-the-shelf (COTS) simulation packages (CSPs). Through the use of a case study developed in conjunction with Ford Motor Company, the paper demonstrates how experimentation with the CSP Witnesstrade and FIRST can achieve a linear speedup when WinGridtrade is used to harness idle PC computing resources. This, combined with the lessons learned from the case study, has encouraged us to develop the Web service extensions to WinGridtrade. It is hoped that this would facilitate wider acceptance of WinGridtrade among enterprises having stringent security policies in place
The financial clouds review
This paper demonstrates financial enterprise portability, which involves moving entire application services from desktops to clouds and between different clouds, and is transparent to users who can work as if on their familiar systems. To demonstrate portability, reviews for several financial models are studied, where Monte Carlo Methods (MCM) and Black Scholes Model (BSM) are chosen. A special technique in MCM, Least Square Methods, is used to reduce errors while performing accurate calculations. The coding algorithm for MCM written in MATLAB is explained. Simulations for MCM are performed on different types of Clouds. Benchmark and experimental results are presented for discussion. 3D Black Scholes are used to explain the impacts and added values for risk analysis, and three different scenarios with 3D risk analysis are explained. We also discuss implications for banking and ways to track risks in order to improve accuracy. We have used a conceptual Cloud platform to explain our contributions in Financial Software as a Service (FSaaS) and the IBM Fined Grained Security Framework. Our objective is to demonstrate portability, speed, accuracy and reliability of applications in the clouds, while demonstrating portability for FSaaS and the Cloud Computing Business Framework (CCBF), which is proposed to deal with cloud portability
High-Performance Cloud Computing: A View of Scientific Applications
Scientific computing often requires the availability of a massive number of
computers for performing large scale experiments. Traditionally, these needs
have been addressed by using high-performance computing solutions and installed
facilities such as clusters and super computers, which are difficult to setup,
maintain, and operate. Cloud computing provides scientists with a completely
new model of utilizing the computing infrastructure. Compute resources, storage
resources, as well as applications, can be dynamically provisioned (and
integrated within the existing infrastructure) on a pay per use basis. These
resources can be released when they are no more needed. Such services are often
offered within the context of a Service Level Agreement (SLA), which ensure the
desired Quality of Service (QoS). Aneka, an enterprise Cloud computing
solution, harnesses the power of compute resources by relying on private and
public Clouds and delivers to users the desired QoS. Its flexible and service
based infrastructure supports multiple programming paradigms that make Aneka
address a variety of different scenarios: from finance applications to
computational science. As examples of scientific computing in the Cloud, we
present a preliminary case study on using Aneka for the classification of gene
expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape
Virtual Machines and Networks - Installation, Performance Study, Advantages and Virtualization Options
The interest in virtualization has been growing rapidly in the IT industry
because of inherent benefits like better resource utilization and ease of
system manageability. The experimentation and use of virtualization as well as
the simultaneous deployment of virtual software are increasingly getting
popular and in use by educational institutions for research and teaching. This
paper stresses on the potential advantages associated with virtualization and
the use of virtual machines for scenarios, which cannot be easily implemented
and/or studied in a traditional academic network environment, but need to be
explored and experimented by students to meet the raising needs and
knowledge-base demanded by the IT industry. In this context, we discuss various
aspects of virtualization - starting from the working principle of virtual
machines, installation procedure for a virtual guest operating system on a
physical host operating system, virtualization options and a performance study
measuring the throughput obtained on a network of virtual machines and physical
host machines. In addition, the paper extensively evaluates the use of virtual
machines and virtual networks in an academic environment and also specifically
discusses sample projects on network security, which may not be feasible enough
to be conducted in a physical network of personal computers; but could be
conducted only using virtual machines
A methodology for testing virtualisation security
There is a growing interest in virtualisation due to its central role in cloud computing, virtual desktop environments and Green IT. Data centres and cloud computing utilise this technology to run multiple operating systems on one physical server, thus reducing hardware costs. However, vulnerabilities in the hypervisor layer have an impact on any virtual machines running on top, making security an important part of virtualisation. In this paper, we evaluate the security of virtualisation, including detection and escaping the environment. We present a methodology to investigate if a virtual machine can be detected and further compromised, based upon previous research. Finally, this methodology is used to evaluate the security of virtual machines. The methods used to evaluate the security include analysis of known vulnerabilities and fuzzing to test the virtual device drivers on three different platforms: VirtualBox, Hyper-V and VMware ESXI. Our results demonstrate that the attack surface of virtualisation is more prone to vulnerabilities than the hypervisor. Comparing our results with previous studies, each platform withstood IOCTL and random fuzzing, demonstrating that the platforms are more robust and secure than previously found. By building on existing research, the results show that security in the hypervisor has been improved. However, using the proposed methodology in this paper it has been shown that an attacker can easily determine that the machine is a virtual machine, which could be used for further exploitation. Finally, our proposed methodology can be utilised to effectively test the security of a virtualised environment
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Developing a grid computing system for commercial-off-the-shelf simulation packages
Today simulation is becoming an increasingly
pervasive technology across major business
sectors. Advances in COTS Simulation Packages
and Commercial Simulation Software have made
it easier for users to build models, often of large complex processes. These two factors combined are to be welcomed and when used correctly can be of great benefit to organisations that make use of the technology. However, it is also the case
that users hungry for answers do not always have the time, or possibly the patience, to wait for results from multiple replications and multiple experiments as standard simulation practice would demand. There is therefore a need to support this advance in the use of simulation within todayâs business with improved computing technology. Grid computing has been put forward as a potential commercial solution to this requirement. To this end, Saker Solutions and the Distributed Systems Research Group at Brunel University have developed a dedicated Grid Computing System (SakerGrid) to support the deployment of simulation models across a desktop grid of PCs. The paper identifies route taken to solve this challenging issue and suggests where the future may lie for this exciting integration of two effective but underused technologies
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