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

数据量和复杂性的爆炸性增长加剧了管理海量数据所面临的关键挑战，从根本上提高了数据的易用性和使用效率。亿级存储系统一般依赖于层次结构的命名空间，这导致了严重的性能瓶颈，并使其难以支持对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不能根据数据量和复杂性的爆炸性增长进行扩展。因此，基于目录树的层次化命名空间已经成为当今大规模文件系统的限制性、难以使用和可扩展性受限的问题。该项目研究了一种新的语义感知命名空间方案，以提供动态和自适应的命名空间管理，并支持Exascale文件系统中的典型文件操作。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，最终目标是高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主干之一，语义感知技术也使大量科学和工程数据密集型应用程序受益。该项目加强了联合国后勤基地和乌缅因州正在进行的高性能计算基础设施的开发。该项目加强了参与机构的本科生和研究生教育，并通过正在进行的NSF资助的ITEST计划扩展到UMaine的K-12。

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.