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Towards Self-Tuning Data Placement in Parallel Database Systems
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Mong-Li Lee,
Masaru Kitsuregawa,
Beng Chin Ooi,
Kian-Lee Tan, and
Anirban Mondal
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Abstract
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Parallel database systems are increasingly being deployed to support the performance demands of end-users. While declustering data across multiple nodes facilitates parallelism, initial data placement may not be optimal due to skewed workloads and changing access patterns. To prevent performance degradation, the placement of data must be reorganized, and this must be done on-line to minimize disruption to the system.
In this paper, we consider a dynamic self-tuning approach to reorganization in a shared nothing system. We introduce a new index-based method that facilitates fast and efficient migration of data. Our solution incorporates a globably height-balanced structure and load tracking at different levels of granularity. We conducted an extensive performance study, and implemented the methods on the Fujitsu AP3000 machine. Both the simulation and empirical results demonstrate that our proposed method is indeed scalable and effective in correcting any deterioration in system throughput.
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References
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BIBTEX
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@inproceedings{DBLP:conf/sigmod/LeeKOTM00,
author = {Mong-Li Lee and
Masaru Kitsuregawa and
Beng Chin Ooi and
Kian-Lee Tan and
Anirban Mondal},
editor = {Weidong Chen and
Jeffrey F. Naughton and
Philip A. Bernstein},
title = {Towards Self-Tuning Data Placement in Parallel Database Systems},
booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on
Management of Data, May 16-18, 2000, Dallas, Texas, USA},
journal = {SIGMOD Record},
publisher = {ACM},
volume = {29},
number = {2},
year = {2000},
isbn = {1-58113-218-2},
pages = {225-236},
crossref = {DBLP:conf/sigmod/2000},
bibsource = {DBLP, http://dblp.uni-trier.de} } },
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