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Looking at Microsoft production services operating millions of databases,\n            <jats:italic>sample efficiency emerged as a crucial requirement<\/jats:italic>\n            to use tuners on diverse workloads.\n          <\/jats:p>\n          <jats:p>This motivates our investigation in LlamaTune, a tuner design that leverages domain knowledge to improve the sample efficiency of existing optimizers. LlamaTune employs an automated dimensionality reduction technique based on randomized projections, a biased-sampling approach to handle special values for certain knobs, and knob values bucketization, to reduce the size of the search space. LlamaTune compares favorably with the state-of-the-art optimizers across a diverse set of workloads. It identifies the best performing configurations with up to 11X fewer workload runs, and reaching up to 21% higher throughput. We also show that benefits from LlamaTune generalize across both BO-based and RL-based optimizers, as well as different DBMS versions.<\/jats:p>\n          <jats:p>While the journey to perform database tuning at cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS tuning practical at scale.<\/jats:p>","DOI":"10.14778\/3551793.3551844","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T22:25:03Z","timestamp":1664490303000},"page":"2953-2965","source":"Crossref","is-referenced-by-count":62,"title":["LlamaTune"],"prefix":"10.14778","volume":"15","author":[{"given":"Konstantinos","family":"Kanellis","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Ding","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Kroth","sequence":"additional","affiliation":[{"name":"Microsoft Gray Systems Lab"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Microsoft Gray Systems Lab"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Curino","sequence":"additional","affiliation":[{"name":"Microsoft Gray Systems Lab"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivaram","family":"Venkataraman","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Apache Cassandra Documentation. 2022. https:\/\/cassandra.apache.org\/doc\/latest\/cassandra\/configuration\/cass_yaml_file.html.  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Documentation for \/proc\/sys\/kernel\/. 2022. https:\/\/www.kernel.org\/doc\/html\/latest\/admin-guide\/sysctl\/kernel.html#perf-cpu-time-max-percent."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687767"},{"key":"e_1_2_1_12_1","volume-title":"The Design and Operation of CloudLab. In 2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Duplyakin Dmitry","year":"2019","unstructured":"Dmitry Duplyakin , Robert Ricci , Aleksander Maricq , Gary Wong , Jonathon Duerig , Eric Eide , Leigh Stoller , Mike Hibler , David Johnson , Kirk Webb , Aditya Akella , Kuangching Wang , Glenn Ricart , Larry Landweber , Chip Elliott , Michael Zink , Emmanuel Cecchet , Snigdhaswin Kar , and Prabodh Mishra . 2019 . The Design and Operation of CloudLab. In 2019 USENIX Annual Technical Conference (USENIX ATC 19) . 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Storm, Christian Garcia-Arellano, Sam S. Lightstone, Yixin Diao, and M. Surendra. 2006. Adaptive Self-Tuning Memory in DB2. In Proceedings of the 32nd International Conference on Very Large Data Bases (Seoul, Korea) (VLDB '06). VLDB Endowment, 1081--1092."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450992"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3484224.3484236"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/3013558.3013569"},{"key":"e_1_2_1_36_1","volume-title":"The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Llama&oldid=1073927386 [Online","author":"Wikipedia Wikipedia","year":"2022","unstructured":"Wikipedia contributors. 2022. Llama --- Wikipedia , The Free Encyclopedia. https:\/\/en.wikipedia.org\/w\/index.php?title=Llama&oldid=1073927386 [Online ; accessed 28- February - 2022 ]. 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