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Data management across geographically distributed autonomous systems: architecture, implementation, and performance evaluation

conference contribution
posted on 2019-01-01, 00:00 authored by D Sun, G Zhang, Shang GaoShang Gao
© 2019 IEEE. The issue of highly efficient geographically-distributed autonomous data management is one of the critical obstacles for opening up a big data era. It creates the need for investigating autonomous and distributed data management systems in big data environments. In this paper, a distributed autonomous data management system is put forward, exhibiting the following features. (1) A distributed architecture designed to meet the requirements of autonomous data management by allowing interconnection, intercommunication, and interoperation of multiple sites over the Internet. (2) An autonomous, multi-level, unstructured data storage system to meet high-efficiency storage needs, with reference to the distributed heterogeneous data storage theories. (3) A distributed autonomous data indexing and retrieval system to support metadata search & full-text searching, fast loading, remote access, and unified view. Experimental and application results demonstrate that the proposed system has high potential to reduce access time and improve storage efficiency, while maintaining satisfactory availability and scalability.

History

Pagination

2284-2292

Location

Zhangjiajie, China

Start date

2019-08-10

End date

2019-08-12

ISBN-13

9781728120584

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

HPCC/SmartCity/DSS 2019 : IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems

Event

HPCC/SmartCity/DSS. Conferences (21st: 17th : 5th : 2019 : Zhangjiajie, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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