Optimal response time retrieval of replicated data (extended abstract)
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Partitioning similarity graphs: a framework for declustering problems
Information Systems
Fast concurrent access to parallel disks
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
A Hypergraph Based Approach to Declustering Problems
Distributed and Parallel Databases
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
Replicated declustering for arbitrary queries
Proceedings of the 2004 ACM symposium on Applied computing
Hypergraph Models and Algorithms for Data-Pattern-Based Clustering
Data Mining and Knowledge Discovery
Iterative-improvement-based declustering heuristics for multi-disk databases
Information Systems
Design Theoretic Approach to Replicated Declustering
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Information Sciences: an International Journal
Analysis and Comparison of Replicated Declustering Schemes
IEEE Transactions on Parallel and Distributed Systems
Generalized Optimal Response Time Retrieval of Replicated Data from Storage Arrays
ACM Transactions on Storage (TOS)
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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.