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

数据量和复杂性的爆炸式增长加剧了大规模数据管理面临的关键挑战，从根本上提高了数据使用的便利性和有效性。Exascale存储系统通常依赖于分层结构的名称空间，这会导致严重的性能瓶颈，并且难以支持对多维属性的实时查询。因此，现有的以分层目录树结构为特征的存储系统，在数据量和数据复杂性爆炸性增长的情况下，已无法进行扩展。因此，基于目录树的分层名称空间变得有限制，难以使用，并且在当今的大规模文件系统的可伸缩性方面受到限制。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文件系统中典型的基于文件的操作。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名称空间管理，其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，语义感知技术也有利于大量科学和工程数据密集型应用程序。该项目加强了联合国大学和缅因州正在进行的高性能计算基础设施的开发。该项目通过美国国家科学基金会资助的ITEST项目，加强了参与机构的本科和研究生教育，并扩展到缅因州的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.