Collaborative Research: CC-NIE Integration: An Open Cloud Infrastructure for Scalable Data Intensive Collaboration

协作研究：CC-NIE 集成：用于可扩展数据密集型协作的开放云基础设施

• 批准号: 1246282
• 负责人: Douglas Swany
• 金额: $ 56.95万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1246282&HistoricalAwards=false
• 关键词: Collaborative; Research; CC; NIE; Integration

By all accounts, the size of the data flows pulsing out of new instruments, sensor networks and massive simulations continues to escalate dramatically. Consequently many researchers in large national and international collaborations, sitting in different administrative domains, spread across the wide area network, and using diverse local resources, are likely to find that the data sets of most current interest are not where they need to be, when they need to be there, for their work to proceed efficiently. It is clear that an adequate cyberinfrastructure strategy for campus level bridging must address such critical problems in the area of data logistics.This project creates a Data Logistics Toolkit (DLT) - an easy to install package of production quality software for building WAN-enabled storage nodes that dramatically improve the ability of data intensive research communities to collaborate in the wide area. DLT-enabled services (e.g. a global "drop box" for research that supports automatic data positioning) enable the automation and optimization of the timely placement of data across a wide range of scenarios, using policy-controlled sharing, replication, caching, control loop optimization and overlay multicast. Offering familiar user and programming interfaces, and backed by a solid but scalable security framework, the DLT transforms global data-intensive collaboration by providing a generic interface to an essential logistical resource - storage - and by giving campuses with DLT-enabled nodes the network awareness and adaptivity needed to maximize the productivity of their wide area links to distributed data and computing resources.

众所周知，数据流的大小从新仪器，传感器网络和大规模模拟中脉动脉动持续急剧升级。因此，许多大型国家和国际合作的研究人员，位于不同的行政领域，分布在大广域网络中，并使用各种本地资源，可能会发现，最新兴趣的数据集并不是他们需要的地方，需要在需要的时候，以便他们的工作有效地进行。显然，校园水平桥接的足够的网络基础策略必须解决数据物流领域的此类关键问题。本项目创建了一个数据物流工具包（DLT） - 易于安装的生产质量软件，用于构建启用WAN的存储节点，以极大地改善数据密集型研究社区的能力，以协作在广阔的区域中协作。使用策略控制的共享，复制，缓存，控制循环优化和覆盖Multicast的Multicast，使用启用DLT的服务（例如，支持自动数据定位的全局“下降框”）使数据及时放置在各种方案中的及时放置数据的自动化和优化。 DLT提供熟悉的用户和编程界面，并在可靠但可扩展的安全框架的支持下，通过为基本的后勤资源（存储）提供通用接口，并通过提供启用DLT的节点的校园来改变全球数据密集型协作，而具有DLT节点的校园，网络意识和适应性需要最大程度地提高其广场链接到其广泛的领域链接，以使其广泛的领域链接与分布数据和计算资源的生产力最大化。