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: 1245918
• 负责人: Paul Sheldon
• 金额: $ 25万
• 依托单位: Vanderbilt 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=1245918&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发出的数据流规模持续急剧增加。因此，大型国家和国际合作中的许多研究人员，坐在不同的管理领域，分布在广域网中，并使用不同的本地资源，很可能会发现当前最感兴趣的数据集不在他们需要的地方，当他们需要在那里，以便他们的工作能够有效地进行。显然，针对校园级桥接的适当网络基础设施策略必须解决数据物流领域的此类关键问题。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，可显着提高数据密集型研究社区在广泛领域内协作的能力。支持 DLT 的服务（例如支持自动数据定位的全球研究“投递箱”）使用策略控制的共享、复制、缓存、控制循环，可以在各种场景中实现数据及时放置的自动化和优化优化和覆盖组播。 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，通过为基本后勤资源（存储）提供通用接口，并为具有支持 DLT 的节点的园区提供网络感知，改变了全球数据密集型协作最大限度地提高与分布式数据和计算资源的广域链接的生产力所需的适应性。