Open AccessIMPLEMENTATION AND EVALUATION OF RUNTIME DATA DECLUSTERING METHOD OVER SAN-CONNECTED PC CLUSTER
Author(s) -
Masato Oguchi,
Masaru Kitsuregawa
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.1.2.123
Subject(s) - bottleneck , computer science , cluster (spacecraft) , node (physics) , operating system , data access , connected component , computer cluster , distributed computing , parallel computing , computer network , embedded system , database , structural engineering , artificial intelligence , engineering
In this paper, a PC cluster connected with Storage Area Network (SAN) is built and evaluated. In the case of SANconnected cluster, each node can access all shared disks directly without LAN; thus, SANconnected clusters achieve better performance than LANconnected clusters for disk access operations. However, if a lot of nodes access the sameshared disk simultaneously, application performance degrades due to I/Obottleneck. A runtime data declustering method, in which data is declustered to several other disks dynamically during the execution of application, is proposed to resolve this problem. Parallel data mining is implemented and evaluated on the SANconnected PC cluster. This application requires iterative scans of a shared disk, which degrade execution performance severely due to I/Obottleneck. The runtime data declustering method is applied to this case. According to the results of experiments, the proposed method prevents performance degradation caused by shared disk bottleneck in SANconnected clusters.