@inproceedings{DBLP:conf/vldb/ChenRS95, author = {Ling Tony Chen and Doron Rotem and Sridhar Seshadri}, editor = {Umeshwar Dayal and Peter M. D. Gray and Shojiro Nishio}, title = {Declustering Databases on Heterogeneous Disk Systems}, booktitle = {VLDB'95, Proceedings of 21th International Conference on Very Large Data Bases, September 11-15, 1995, Zurich, Switzerland}, publisher = {Morgan Kaufmann}, year = {1995}, isbn = {1-55860-379-4}, pages = {110-121}, ee = {db/conf/vldb/ChenRS95.html}, crossref = {DBLP:conf/vldb/95}, bibsource = {DBLP, http://dblp.uni-trier.de} }
Declustering is a well known strategy to achieve maximum I/O parallelism in multi- disk systems. Many declustering methods have been proposed for symmetrical disk systems,i.e., multi-disk systems in which all disks have the same speed and capacity. This work deals with the problem of adapting such declustering methods to work in heterogeneous environments. In such environments there are many types of disks and servers with a large range of speeds and capacities. We deal first with the case of perfectly declustered queries, i.e., queries which retrieve a fixed proportion of the answer from each disk.
We propose an algorithm which determines the fraction of the dataset whichmust be loaded on each disk. The algorithm may be tailored to find disk loading for minimal response time for a given database size, or to compute a system profile showing the optimal loading of the disks for all possible ranges of database sizes. The methods proposed here are general and can be used in conjunction with most known symmetric declustering methods.
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