Extensions of an Empirical Automated Tuning Framework

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dc.contributor.advisorHollingsworth, Jeffery Ken_US
dc.contributor.authorZhou, Yifanen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2013-07-04T05:32:58Z
dc.date.available2013-07-04T05:32:58Z
dc.date.issued2013en_US
dc.description.abstractEmpirical auto-tuning has been successfully applied to scientific computing applications and web-based cluster servers over the last few years. However, few studies are focused on applying this method on optimizing the performance of database systems. In this thesis, we present a strategy that uses Active Harmony, an empirical automated tuning framework to optimize the throughput of PostgreSQL server by tuning its settings such as memory and buffer sizes. We used Nelder-Mead simplex method as the search engine, and we showed how our strategy performs compared to the hand-tuned and default results. Another part of this thesis focuses on using data from prior runs of auto-tuning. Prior data has been proved to be useful in many cases, such as modeling the search space or finding a good starting point for hill-climbing. We present several methods that were developed to manage the prior data in Active Harmony. Our intention was to provide tuners a complete set of information for their tuning tasks.en_US
dc.identifier.urihttp://hdl.handle.net/1903/14298
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledComputer engineeringen_US
dc.subject.pquncontrolledActive Harmonyen_US
dc.subject.pquncontrolledAutomated Tuningen_US
dc.subject.pquncontrolledDatabase Systemsen_US
dc.subject.pquncontrolledEmpiricalen_US
dc.subject.pquncontrolledNelder-Mead Simplex Methoden_US
dc.titleExtensions of an Empirical Automated Tuning Frameworken_US
dc.typeThesisen_US

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