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一个合作实验室建议开发一个研究和教育数据仓库网络(REDDnet)，以满足对共享的广域存储基础设施的需求，在该基础设施中可以缓冲数据流，以便进行协作处理，包括分析、简化、可视化和探索。当科学或其他数据量变得很大时，一般的“数据物流”问题就会出现，每天的数据量达到1TB或更多。高性能网络帮助用户更快地移动他们的数据，但用户通常没有地方放下他们的TB数据来处理或与他人共享。REDDnet将由一组8个大型存储节点组成，战略性地分布在全国高性能研究网络中，并配置有一种名为物流网络(LN)的分布式存储管理技术，专门设计用于解决数据物流的主要问题。它的预计容量超过320 TB，将由一组研究人员和教育工作者使用，他们需要管理大型数据集，并已确定需要进行分布式协作。REDDnet的首批用户包括两个“千万亿级”粒子物理实验，与全球约5000名合作者合作(ATLAS和CMS)，一个从卫星传感器挖掘信息的全国性社区(AmericaView)，一个由八所大学和橡树岭国家实验室组成的联盟，进行大规模的超新星模拟(Terascale超新星计划)，以及Vanderbilt和Lawrence Berkeley国家实验室合作研究生物分子结构(大分子组装的图像重建)。REDDnet的两个主要组织合作者，Vanderbilt的研究和教育高级计算中心(ACCRE)和americaView(AV)，都有教育使命和正在进行的活动，可以很好地利用REDDnet在教育和推广方面的巨大潜力。