Browsing by Subject "Data items"

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Open Access
Evaluation of a broadcast scheduling algorithm
(Springer, Berlin, Heidelberg, 2001) Karakaya, M.; Ulusoy, Özgür
One of the two main approaches of data broadcasting is pull- based data delivery. In this paper, we focus on the problem of scheduling data items to broadcast in such a pull-based environment. Previous work has shown that the Longest Wait First heuristic has the best performance results compared to all other broadcast scheduling algorithms, however the decision overhead avoids its practical implementation. Observing this fact, we propose an efficient broadcast scheduling algorithm which is based on an approximate version of the Longest Wait First heuristic. We also compare the performance of the proposed algorithm against well- known broadcast scheduling algorithms. © Springer-Verlag Berlin Heidelberg 2001.
Open Access
Selective replicated declustering for arbitrary queries
(Springer, 2009-08) Oktay, K. Yasin; Türk, Ata; Aykanat, Cevdet
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.