Acquisition of a Data Depot Network for Wide Area, Data Intensive Collaboration

收购数据仓库网络以实现广域数据密集型协作

• 批准号: 0619847
• 负责人: Paul Sheldon
• 金额: $ 59.43万
• 依托单位: Vanderbilt University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-10-01 至 2011-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0619847&HistoricalAwards=false
• 关键词: Acquisition; Data; Depot; Network; Wide

A Vanderbilt University-led collaboratory proposes to develop a Research and Education Data Depot Network (REDDnet) in response to the need for a shared, wide-area storage infrastructure where data flows can be buffered for collaborative processing, including analysis, reduction, visualization, exploration. The general "data logistics" problem arises when quantities of scientific or other data become large, at the level of 1TB or more per day. High performance networks help users move their data faster, but users routinely have nowhere to put their terabyte down to work on it or share it with someone. REDDnet will consist of a set of eight large storage nodes, strategically positioned across the nation's high performance research networks, and configured with a distributed storage management technology, called Logistical Networking (LN), expressly designed to attack major problems of data logistics. With more than 320 TB of projected capacity, it will be used by a diver set of researchers and educators with large datasets to manage and an established need for distributed collaboration. The initial group of REDDnet users includes two "petascale" particle physics experiments with approximately 5000 collaborators world-wide, (ATLAS and CMS), a nationwide community (AmericaView) mining information from satellite-based sensors, a consortium of eight universities and Oak Ridge National Laboratory doing large scale simulations of supernovas (the Terascale Supernova Initiative), and a collaborative effort between a Vanderbilt and Lawrence Berkeley National Laboratory investigating the structure of biological molecules (Image Reconstruction of Large Macromolecular Assemblies). The two main organizational collaborators in REDDnet, Vanderbilt's Advanced Computing Center for Research and Education (ACCRE) and AmericaView (AV), have educational missions and ongoing activities that are well positioned to exploit the significant potential of REDDnet for education and outreach.

范德比尔特大学领导的合作社提议开发研究和教育数据仓库网络（REDDNET），以回应需要共享的，广阔的区域存储基础架构，在该基础架构中可以缓冲数据流以进行协作处理，包括分析，还原，可视化，可视化，探索。当每天1TB或更多水平的科学或其他数据变大时，就会出现一般的“数据后勤”问题。 高性能网络可帮助用户更快地将数据移动，但是用户通常无处可将其Terabyte放下来处理或与某人共享。 REDDNET将由八个大型存储节点组成，该节点策略性地位于全国高性能研究网络中，并配置了分布式存储管理技术，称为后勤网络（LN），明确设计为攻击数据物流的重大问题。具有超过320 TB的预计能力，将由一组具有大量数据集的研究人员和教育者使用，并建立了分布式协作的需求。 The initial group of REDDnet users includes two "petascale" particle physics experiments with approximately 5000 collaborators world-wide, (ATLAS and CMS), a nationwide community (AmericaView) mining information from satellite-based sensors, a consortium of eight universities and Oak Ridge National Laboratory doing large scale simulations of supernovas (the Terascale Supernova Initiative), and a collaborative effort between a Vanderbilt and劳伦斯·伯克利（Lawrence Berkeley）国家实验室研究生物分子的结构（大型大分子组件的图像重建）。 Reddnet的两个主要组织合作者，Vanderbilt的高级研究与教育计算中心（ACPRE）和Americaview（AV），具有教育任务和正在进行的活动，这些活动旨在利用Reddnet在教育和外展方面的重要潜力。