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They either insufficiently use previous tuning experience by treating successively tuning independently, or explore the configuration space aggressively, violating the Service Level Agreements (SLA).<\/jats:p><jats:p>To address the above problems, we propose ContTune, a continuous tuning system for stream applications. It is equipped with a novel Big-small algorithm, in which the Big phase decouples the tuning from the topological graph by decomposing the job tuning problem into sub-problems that can be solved concurrently. We propose a conservative Bayesian Optimization (CBO) technique in the Small phase to speed up the tuning process by utilizing the previous observations. It leverages the state-of-the-art (SOTA) tuning method as conservative exploration to avoid SLA violations. Experimental results show that ContTune reduces up to 60.75% number of reconfigurations under synthetic workloads and up to 57.5% number of reconfigurations under real workloads, compared to the SOTA method DS2.<\/jats:p>","DOI":"10.14778\/3625054.3625064","type":"journal-article","created":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T17:09:42Z","timestamp":1701709782000},"page":"4282-4295","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems"],"prefix":"10.14778","volume":"16","author":[{"given":"Jinqing","family":"Lian","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, and National Engineering Laboratory for Big Data Analysis and Applications, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenglin","family":"Pu","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Xiang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yawen","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Computer Science, and National Engineering Laboratory for Big Data Analysis and Applications, Peking University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2019. 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