Selective replicated declustering for arbitrary queries

Date
2009-08
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Source Title
European Conference on Parallel Processing. Euro-Par 2009: Euro-Par 2009 Parallel Processing
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Publisher
Springer
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Pages
375 - 386
Language
English
Type
Conference Paper
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Abstract

Data declustering is used to minimize query response times in data intensive applications. In this technique, query retrieval process is parallelized by distributing the data among several disks and it is useful in applications such as geographic information systems that access huge amounts of data. Declustering with replication is an extension of declustering with possible data replicas in the system. Many replicated declustering schemes have been proposed. Most of these schemes generate two or more copies of all data items. However, some applications have very large data sizes and even having two copies of all data items may not be feasible. In such systems selective replication is a necessity. Furthermore, existing replication schemes are not designed to utilize query distribution information if such information is available. In this study we propose a replicated declustering scheme that decides both on the data items to be replicated and the assignment of all data items to disks when there is limited replication capacity. We make use of available query information in order to decide replication and partitioning of the data and try to optimize aggregate parallel response time. We propose and implement a Fiduccia-Mattheyses-like iterative improvement algorithm to obtain a two-way replicated declustering and use this algorithm in a recursive framework to generate a multi-way replicated declustering. Experiments conducted with arbitrary queries on real datasets show that, especially for low replication constraints, the proposed scheme yields better performance results compared to existing replicated declustering schemes. © 2009 Springer.

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Keywords
Data declustering, Data items, Data replica, Data-intensive application, Declustering, Declustering scheme, Iterative improvements, Query distributions, Query information, Query response, Query retrieval, Real data sets, Response time, Selective replication, Very large datum, Artificial intelligence, Bioinformatics, Disks (machine components), Disks (structural components), Distributed computer systems, Geographic information systems, Response time (computer systems)
Citation
Published Version (Please cite this version)