SGER: IMR: Development of Scientific Computing in the Cloud for Research and Education

非技术摘要“云计算”是一个最近获得了突出地位的术语，用于描述位于许多不同位置的大量计算资源的使用。这些资源通常由第三方组织拥有，并根据每台计算机的功能而不是使用中的底层硬件提供给消费者。这种模式几乎立即在Web服务托管领域证明了它的成功，这是大多数云服务提供商的主要目标。我们的目标是确定这些相同的资源是否可以用于科学目的。因此，我们建议开发用于科学计算的云计算应用程序，并使目前缺乏足够计算资源的工作科学家能够进行这种计算。现代科学应用程序需要高性能的计算设施，这是超出了许多从业者谁缺乏经验和资金来完成严重的科学计算，即使他们的工作可能会提高目前可行的calculation.Technical AbstractThe目标的资源虚拟化是离婚工作流程的地方限制，从而使其能够自由迁移可用的物理资源。这种灵活性产生了巨大的调度优势，因为可以放置作业并经常进行迁移，以便最有效地利用可用硬件。资源虚拟化当然不是什么新鲜事。例如，Condor允许用户访问大型计算资源池，其中每个计算资源都可以是完全唯一的硬件配置。Condor的虚拟化策略是“欺骗”应用程序，使其认为它在主计算机上。缺点是Condor必须替换特定于体系结构和操作系统的系统库。因此，支持此策略可能非常耗时。另一种虚拟化策略使用？网格计算编写“支持网格”的应用程序意味着编写具有适当协议的应用程序，以便网格中间件可以移动应用程序。这里的缺点是，大多数科学家没有背景或时间来编写网格启用programmes.We认为，在云计算和网格计算之间的管理差异可以发现CC的最有趣和有用的优势的潜力。例如，亚马逊？的EC2（“弹性计算云”）不仅提供了另一个Web服务平台，而且提供了一个完全虚拟的计算机。EC2“计算实例”为您提供了一套全面的虚拟硬件，包括磁盘空间、RAM和IP地址。在这个虚拟平台上，您可以安装自己的操作系统。这一策略的引人注目之处在于，它只虚拟化了硬件。这样做提供了最极端的通用性：如果我可以在我面前的桌面上运行应用程序，那么我可以在Amazon Cloud中运行它。这项工作的目标是测试这个想法，并将这种Amazon Cloud方法的性能和经济性与传统计算集群进行比较。

Non-technical Abstract"Cloud Computing" is a term that has gained prominence recently to describe the use of a large body of compute resources located at many different locations. These resources are typically owned by third-party organizations and are made available to consumers in terms of the capabilities of each computer rather than the underlying hardware in use. This model proved its success almost immediately in the web services hosting domain, the principle target for most cloud service providers. Our aim here is to determine whether these same resources can be adapted for scientific purposes. Thus we propose to develop applications of cloud computing for scientific calculations and making such calculations feasible for working scientists lacking sufficient compute resources at the present. Modern scientific applications require high-performance computing facilities which are beyond the reach of many practitioners who lack both the experience and funding to complete serious scientific calculations, even though their work might be enhanced by currently feasible calculations.Technical AbstractThe goal of resource virtualization is to divorce workflow from locality constraints, thereby allowing it to migrate freely amongst available physical resources. This flexibility yields tremendous scheduling advantages, as jobs can be placed and often migrated in order to make the most efficient use of available hardware. Resource virtualization is certainly nothing new. For example, Condor allows a user to access a large pool of compute resources, each one of which may be a completely unique hardware configuration. Condor's virtualization strategy is to "trick" the application into thinking that it is on the home machine. The drawback is that Condor must replace system libraries, which are architecture- and OS-specific. Hence, this strategy can be very time consuming to support. Another virtualization strategy uses ?grid computing.? Writing an application that is "grid-enabled" means writing an application with the appropriate protocols in place so that it can be moved around by the grid middleware. The disadvantage here is that most scientists do not have the background or the time to write grid-enabled programs.We argue that within administrative differences between Cloud and Grid computing can be found the potential for CC's most intriguing and useful advantages. For instance, Amazon?s EC2 ("Elastic Compute Cloud") provides not just another web services platform, but a fully virtual computer. An EC2 "compute instance" gives you a comprehensive set of virtual hardware, complete with disk space, RAM, and IP address. On this virtual platform, you then install your own operating system. The compelling aspect of this strategy is that it virtualizes exactly what needs to be and no more: the hardware. Doing so provides the utmost extreme in generality: if I can run an application on the desktop sitting in front of me then I can run it in the Amazon Cloud.The goal of this work is to test this idea and compare the performance and economy of this Amazon Cloud approach with a traditional computing cluster.