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2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (2005)
Atlanta, Georgia
Sept. 27, 2005 to Sept. 29, 2005
ISSN: 1526-7539
ISBN: 0-7695-2458-3
pp: 239-248
Eno Thereska , Carnegie Mellon University Pittsburgh, PA
Anastassia Ailamaki , Carnegie Mellon University Pittsburgh, PA
Dushyanth Narayanan , Microsoft Research Cambridge, UK
ABSTRACT
<p>Administration tasks increasingly dominate the total cost of ownership of database management systems. A key task, and a very difficult one for an administrator, is to justify upgrades of CPU, memory and storage resources with quantitative predictions of the expected improvement in workload performance. Current database systems are not designed with such prediction in mind and hence offer only limited help to the administrator. This paper proposes changes to database system design that enable a Resource Advisor to answer "what-if" questions about resource upgrades. A prototype Resource Advisor built to work with a commercial DBMS shows the efficacy of our approach in predicting the effect of upgrading a key resource - buffer pool size - on OLTP workloads in a highly concurrent system.</p>
INDEX TERMS
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CITATION
Eno Thereska, Anastassia Ailamaki, Dushyanth Narayanan, "Continuous resource monitoring for self-predicting DBMS", 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, vol. 00, no. , pp. 239-248, 2005, doi:10.1109/MASCOT.2005.21
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