A metadata access strategy of learning resources based on HDFS

Author

Gao, Yaqi ; Zheng, Shijue

Author_Institution

Dept. of Comput. Sci., Central China Normal Univ., Wuhan, China

fYear

2011

fDate

21-23 Oct. 2011

Firstpage

620

Lastpage

622

Abstract

According to the existing project of visual cloud computing mobile learning platform and considering the performance bottlenecks of NameNode in the Hadoop Distributed File System (HDFS), e add a metadata server (MDS) to share part of the metadata access pressure when the load of NameNode is too large, to optimize the performance of the overall system. This paper details the improved metadata access strategy; compares the differences of overall system performances between the improved one and the original one through performance tests. The conclusion is that the improved metadata access strategy can improve the overall system performance when the NameNode is under great pressure.

Keywords

cloud computing; computer aided instruction; distributed databases; meta data; mobile computing; network operating systems; HDFS; Hadoop distributed file system; NameNode; learning resources; metadata access pressure; metadata access strategy; metadata server; visual cloud computing mobile learning platform; Ad hoc networks; Cloud computing; Computer architecture; File systems; Mobile communication; Servers; Visualization; HDFS; Metadata Server; metadata;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Analysis and Signal Processing (IASP), 2011 International Conference on

Conference_Location

Hubei

Print_ISBN

978-1-61284-879-2

Type

conf

DOI

10.1109/IASP.2011.6109119

Filename

6109119