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

“云计算”是最近获得突出的一个术语，用来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所使用的底层硬件向消费者提供。这种模式在web服务托管领域（大多数云服务提供商的主要目标）几乎立即证明了它的成功。我们在这里的目的是确定这些相同的资源是否可以用于科学目的。因此，我们建议开发云计算在科学计算中的应用，并使这种计算在目前缺乏足够计算资源的在职科学家中可行。现代科学应用需要高性能的计算设备，这是许多缺乏经验和资金来完成严肃科学计算的实践者所无法企及的，即使他们的工作可以通过目前可行的计算来加强。技术摘要资源虚拟化的目标是将工作流从局部性约束中分离出来，从而允许工作流在可用的物理资源之间自由迁移。这种灵活性产生了巨大的调度优势，因为可以放置和经常迁移作业，以便最有效地利用可用的硬件。资源虚拟化当然不是什么新鲜事物。例如，Condor允许用户访问一个大的计算资源池，其中每个资源池可能是一个完全独特的硬件配置。Condor的虚拟化策略是“欺骗”应用程序，使其认为它在家用机器上。缺点是Condor必须替换系统库，这些库是特定于体系结构和操作系统的。因此，支持此策略可能非常耗时。另一种虚拟化策略的用途是？网格计算。编写“支持网格”的应用程序意味着使用适当的协议编写应用程序，以便网格中间件可以移动它。这里的缺点是，大多数科学家没有背景或时间来编写支持网格的程序。我们认为，在云和网格计算之间的管理差异中，可以发现CC最有趣和最有用的优势的潜力。比如，亚马逊？微软的EC2（“弹性计算云”）不仅提供了另一个网络服务平台，而且提供了一个完全虚拟的计算机。EC2“计算实例”为您提供了一组全面的虚拟硬件，包括磁盘空间、RAM和IP地址。然后在这个虚拟平台上安装自己的操作系统。这种策略的引人注目之处在于，它完全虚拟化了需要虚拟化的东西，而不是硬件。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，那么我就可以在亚马逊云上运行它。这项工作的目标是测试这个想法，并将这种Amazon Cloud方法与传统计算集群的性能和经济性进行比较。

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.