MRI: Acquisition of a Shared Scalable Research Storage System

该项目收购了一个高端可扩展网络存储系统，旨在将一种创新的存储架构（MEMORI）集成到该机构现有的研究计算基础设施中，以满足传统和非传统HPC研究人员的需求。然后，支持者将通过获取高端可扩展网络存储系统来实例化该体系结构。这些仪器将被集中管理，但与整个研究界共享。该仪器使网络安全、心理健康、气候动力学和社会系统方面的各种项目成为可能，所有这些都对国家的健康和经济福祉产生积极影响。特别是，该存储系统旨在支持姊妹机构的共享访问，因为GMU是4-VA大学联盟的成员，该联盟提供国家级基础设施，促进资源共享。潜在的外部利益相关者对数据的访问也将得到加强。当前的研究计算基础设施无法支持涉及“大数据”元素的各种当前研究项目不断增长的存储需求。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，提供一个共享的存储服务器，能够支持当前的存储需求，并以经济有效的方式为未来的需求进行扩展，而不是强迫研究项目在个人和特别的基础上处理这些限制。拟议的建筑是专门为支持人文和社会科学以及更传统的科学和工程学科的研究项目而设计的。此外，集中管理仪器为多大学共享访问创建了一个安全的环境，对敏感数据进行了适当的控制，并为数据管理计划、备份和归档创建了一个可靠的框架。此外，许多院系提供数据挖掘、数据分析和数据可视化的课程。这些课程目前在支持大规模数据集方面的能力有限，并且将从拟议的系统中受益匪浅。这些仪器将大大加强图书馆的服务。对系统的快速网络访问提供了存储大型数据库本地副本的能力，供教师和学生进行文本挖掘研究，以及可视化功能。

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.