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Improper settings of configuration parameters are shown to have detrimental effects on performance, reliability and availability of the overall database management system. This is also true for multi-model databases, which use a single platform to support multiple data models. Existing approaches for automatic DBMS knobs tuning are not directly applicable to multi-model databases due to the diversity of multi-model database instances and workloads. Firstly, in cloud environment, they have difficulty adapting to changing environments and diverse workloads. Secondly, they rely on large-scale high-quality training samples that are difficult to obtain. Finally, they focus primarily on throughput metrics, ignoring tuning requirements for resource utilization. Therefore, in this paper, we propose a multi-model database configuration parameters tuning solution named MMDTune. It selects influential parameters, recommends the optimal configurations in a high-dimensional continuous space. For different workloads, the TD3 algorithm is improved to generate reasonable parameter adjustment plans according to the internal state of the multi-model databases. We conduct extensive experiments under 5 different workloads on real cloud databases to evaluate MMDTune. Experimental results show that MMDTune adapts well to a new hardware environment or workloads, and significantly outperforms the representative tuning tools, such as OtterTune, CDBTune.<\/jats:p>","DOI":"10.1007\/s10844-022-00762-0","type":"journal-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T06:47:38Z","timestamp":1669790858000},"page":"167-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Parameters tuning of multi-model database based on deep reinforcement learning"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0005-2073","authenticated-orcid":false,"given":"Feng","family":"Ye","sequence":"first","affiliation":[]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Xiwen","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Nadia","family":"Nedjah","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Shi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Basu, D., Lin, Q., Chen, W., & et al. 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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}