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To this end, we propose\n            <jats:italic>SA-LSM<\/jats:italic>\n            to use (S)urvival (A)nalysis for Log-Structure Merge Tree (LSM-tree) key-value (KV) stores. Conventionally, the data layout of LSM-tree is determined jointly by the write and the compaction operations. However, this process by default does not fully utilize the access information of data records, leading to a suboptimal data layout that negatively impacts the system performance.\n            <jats:italic>SA-LSM<\/jats:italic>\n            utilizes the survival analysis, a statistical learning algorithm commonly used in biostatistics, to optimize the data layout.\n          <\/jats:p>\n          <jats:p>\n            When put into perspective of LSM-tree with proper adoptions,\n            <jats:italic>SA-LSM<\/jats:italic>\n            can accurately predict cold data using the historical semantic information and access traces. As a concrete realization, we implement our proposal in X-Engine, a commercial-strength open-source LSM-tree storage engine. To make the deployment more flexible, we also design a non-intrusive architecture that offloads CPU-intensive work, e.g., model training and inference, to an external service. Extensive experiments on real-world workloads show that it can decrease the tail latency by up to 78.9% compared to the state-of-the-art techniques. The generality of this approach and the significant performance improvement show great potentials in a variety of related applications.\n          <\/jats:p>","DOI":"10.14778\/3547305.3547320","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T16:09:53Z","timestamp":1662566993000},"page":"2161-2174","source":"Crossref","is-referenced-by-count":20,"title":["<i>SA-LSM<\/i>"],"prefix":"10.14778","volume":"15","author":[{"given":"Teng","family":"Zhang","sequence":"first","affiliation":[{"name":"Alibaba Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Tan","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Cai","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianling","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2022. 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