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.

根据所有人的说法，从新仪器、传感器网络和大规模模拟中涌出的数据流的规模继续急剧上升。因此，许多大型国家和国际合作的研究人员分布在广域网络上，分布在不同的行政领域，并使用不同的本地资源，他们可能会发现，最新感兴趣的数据集并不在他们需要的地方，当他们需要那里的时候，他们的工作才能有效地进行。显然，用于园区级桥接的适当的网络基础设施战略必须解决数据物流领域的此类关键问题。该项目创建了数据物流工具包(DLT)，这是一个易于安装的生产质量软件包，用于构建支持广域网的存储节点，从而显著提高数据密集型研究社区在大范围内协作的能力。启用DLT的服务(例如，用于支持自动数据定位的研究的全球“收件箱”)使用策略受控的共享、复制、缓存、控制环路优化和覆盖多播，实现了在各种场景中及时放置数据的自动化和优化。DLT提供熟悉的用户和编程接口，并以坚实但可扩展的安全框架为后盾，通过提供到基本后勤资源(存储)的通用接口，并为具有DLT功能的节点的园区提供所需的网络感知和适应性，最大限度地提高其广域链接到分布式数据和计算资源的生产力，从而转变了全球数据密集型协作。