MRI: Acquisition of a Shared Scalable Research Storage System

该项目收购了高端可扩展网络存储系统，旨在将创新的存储体系结构(存储器)整合到研究所现有的研究计算基础设施中，以满足传统和非传统高性能计算研究人员的需求。然后，支持者将通过收购高端可扩展网络存储系统来实例化该体系结构。仪器将集中管理，但与整个研究社区共享。该仪器使网络安全、心理健康、气候动态和社会系统方面的各种项目得以实施，所有这些项目都对该国的健康和经济福祉产生了积极影响。特别是，存储系统旨在支持姐妹机构共享访问，因为GMU是4-VA大学联盟的成员，该联盟提供促进资源共享的国家级基础设施。潜在的外部利益攸关方获取数据的能力也将得到加强。当前的研究计算基础设施无法支持当前涉及“大数据”元素的各种研究项目日益增长的存储需求。因此，单个项目在存储、挖掘、可视化和共享大型数据集方面的能力受到限制。研究人员将利用存储技术的最新发展，提供能够支持当前存储需求的共享存储服务器，并以经济高效的方式针对未来需求进行扩展，而不是强迫研究项目单独和临时地处理这些限制。拟议的架构是专门为支持人文和社会科学以及较传统的科学和工程学科的研究项目而设计的。此外，集中管理仪器设备可为多所大学共享访问、适当控制敏感数据以及为数据管理计划、备份和归档创建可靠的框架。此外，多个系还开设了数据挖掘、数据分析和数据可视化课程。这些课程目前在支持大规模数据集方面的能力有限，并将从拟议的系统中受益匪浅。该仪器将显著提高图书馆的服务水平。对系统的快速网络访问提供了存储大型数据库的本地副本以供教职员工和学生进行文本挖掘研究的能力，以及可视化能力。

This project, acquiring a high-end scalable network storage system, aims to integrate an innovative storage architecture (MEMORI) into the existing research computing infrastructure at the institution to address the needs of both traditional and non-traditional HPC researchers. The proponents will then instantiate that architecture with the acquisition of the high-end scalable network storage system. The instrumentation will be managed centrally, but shared with the entire research community. The instrumentation enables a variety of projects in cyber-security, mental health, climate dynamics and social systems, all of which positively benefit the health and economic well-being of the country. In particular, the storage system is designed to support shared access by sister institutions, since GMU is a member of the 4-VA university consortium that provides state-level infrastructure promoting resource sharing. Data access by potential outside stakeholders will also be enhanced. The current research computing infrastructure is unable to support the growing storage needs for the wide variety of current research projects involving elements of "big data." As a result, individual projects are limited in their ability to store, mine, visualize and share large data sets. Rather than forcing research projects to deal with these limitations on an individual and ad hoc basis, the researchers will take advantage of recent developments in storage technologies to provide a shared storage server capable of supporting current storage needs and scalable in a cost-effective manner for future needs. The proposed architecture is specifically designed to support research projects in the humanities and social sciences, as well as the more traditional science and engineering disciplines. Furthermore, centrally managing the instrumentation creates a secure environment for multi-university shared access, appropriate control of sensitive data, and a reliable framework for data management plans, backups, and archiving. In addition, a variety of departments offer courses in data mining, data analytics, and data visualization. These courses are currently limited in their ability to support datasets of significant size, and will greatly benefit from the proposed system. The library services will be significantly enhanced by the instrumentation. Fast network access to the system provides the ability to store local copies of large databases for text mining research by faculty and students, as well as visualization capabilities.