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: 1245934
• 负责人: Micah Beck
• 金额: $ 17.18万
• 依托单位: University of Tennessee Knoxville
• 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=1245934&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.

从各方面来看，从新仪器、传感器网络和大规模模拟中产生的数据流的规模继续急剧上升。因此，许多从事大型国家和国际合作的研究人员，分布在不同的行政领域，遍布广域网，并使用各种当地资源，可能会发现最新的数据集并没有在他们需要的地方，当他们需要在那里时，他们的工作才能有效进行。很明显，一个足够的校园级桥接网络基础设施战略必须解决数据物流领域的这些关键问题。该项目创建了一个数据物流工具包（DLT）-一个易于安装的生产质量软件包，用于构建支持WAN的存储节点，大大提高了数据密集型研究社区在广域网中协作的能力。启用DLT的服务（例如，支持自动数据定位的研究的全局“投递箱”）通过使用策略控制的共享、复制、缓存、控制循环优化和覆盖多播，实现了跨各种场景的数据及时放置的自动化和优化。DLT提供熟悉的用户和编程接口，并以可靠但可扩展的安全框架为后盾，通过为基本的后勤资源（存储）提供通用接口，并为具有DLT启用节点的校园提供所需的网络感知和自适应能力，以最大限度地提高其广域链路与分布式数据和计算资源的生产力，从而转变全球数据密集型协作。