CSR: Small: Collaborative Research: SANE: Semantic-Aware Namespace in Exascale File Systems

CSR：小型：协作研究：SANE：Exascale 文件系统中的语义感知命名空间

• 批准号: 1117032
• 负责人: Yifeng Zhu
• 金额: $ 19.09万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117032&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; SANE

Explosive growth in volume and complexity of data exacerbates the key challenge facing the management of massive data in a way that fundamentally improves the ease and efficacy of their usage. Exascale storage systems in general rely on hierarchically structured namespace that leads to severe performance bottlenecks and makes it hard to support real-time queries on multi-dimensional attributes. Thus, existing storage systems, characterized by the hierarchical directory tree structure, are not scalable in light of the explosive growth in both the volume and the complexity of data. As a result, directory-tree based hierarchical namespace has become restrictive, difficult to use, and limited in scalability for today's large-scale file systems.This project investigates a novel semantic-aware namespace scheme to provide dynamic and adaptive namespace management and support typical file-based operations in Exascale file systems. The project leverages semantic correlations among files and exploits the evolution of metadata attributes to support customized namespace management, with the end goal of efficiently facilitating file identification and end users data lookup. This project provides significant performance improvements for existing file systems in Exascale file systems. Since Exascale file systems constitute one of the backbones of the high-performance computing infrastructure, the semantic-aware techniques also benefits a great number of scientific and engineering data-intensive applications. This project strengthens the ongoing development of high performance computing infrastructures at both UNL and UMaine. The project enhances undergraduate and graduate education at both participating institutions and outreach to K-12 in UMaine via an ongoing NSF-funded ITEST program.

数据量和复杂性的爆炸式增长加剧了海量数据管理所面临的关键挑战，从根本上提高了数据使用的易用性和有效性。Exascale存储系统通常依赖于层次结构的命名空间，这导致严重的性能瓶颈，并且难以支持多维属性的实时查询。因此，以分层目录树结构为特征的现有存储系统鉴于数据量和复杂性的爆炸性增长而不可扩展。因此，基于目录树的分层命名空间已成为限制，难以使用，并限制在今天的大规模文件系统的可扩展性。本项目研究了一种新的语义感知命名空间方案，提供动态和自适应的命名空间管理，并支持典型的文件为基础的操作在Exascale文件系统。该项目利用文件之间的语义相关性，并利用元数据属性的演变来支持定制的命名空间管理，最终目标是有效地促进文件识别和最终用户数据查找。该项目为Exascale文件系统中的现有文件系统提供了显著的性能改进。由于Exascale文件系统构成了高性能计算基础设施的骨干之一，因此语义感知技术也使大量科学和工程数据密集型应用受益。该项目加强了UNL和UMaine正在进行的高性能计算基础设施的开发。该项目通过一个正在进行的NSF资助的ITEST计划，加强了参与机构的本科生和研究生教育，并推广到缅因州的K-12。