Metadata Management Architecture Research Articles

A multi‐channel architecture for metadata management in cloud storage systems by binding CPU‐cores to disks

SummaryMetadata operations have become dominant file operations in the storage systems. In the scenarios of read‐more and write‐less of massive small files, the current distributed file systems suffer from the unsatisfying performance and scalability of metadata service because of random disk I/O during metadata operations. In this paper, a highly efficient metadata management architecture for cloud storage systems is proposed. The cluster design significantly improves the scalability of the system. A concept of the disk I/O channel is introduced, which is an independent data storage pipe by binding an independent CPU‐core to each physical disk. In addition, a multi‐channel fast key‐value storage engine is proposed to provide the extremely efficient performance for the underlying storage service, which takes full advantages of multi‐core processors and parallel disks I/O. Besides, a new dynamic load‐balancing strategy is proposed to reduce load thrashing and improve the precision of rebalancing among the clusters. Performance measurements under a variety of benchmarks show that the metadata management is capable of handling the massive small files storage and the performance is improved significantly compared to the existing solutions. Copyright © 2016 John Wiley & Sons, Ltd.

Metadata Management in a Data Staging Repository

DataStaR is a data staging repository currently in development at Cornell University Library designed to support the curation of scientific research data. DataStaR employs a semantic metadata management architecture that provides several key benefits to users and librarians—a Web-based interface to create metadata and export valid XML in multiple standards, the ability to reuse previously created metadata in a straightforward manner, and compatibility with emerging semantic Web technologies. This paper reviews considerations that informed the DataStaR project by presenting high-level use cases. It follows with a description of DataStaR's metadata architecture, focusing on the semantic Web components that facilitate metadata reuse and the creation of metadata according to multiple standards. It concludes with a description of how a user may interact with the system.