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- W3138515424 abstract "Data-intensive systems are typical applications in the big data era. Most data-intensive systems employ LSM (Log-Structured Merge)-tree-based key-value stores, such as RocksDB and LevelDB. The LSM-tree has a multi-level data structure and can transform random writes into sequential ones by a special operation called compaction. However, compaction operations are costly and will increase the processing latency and lower the throughput of the LSM-tree. In this paper, we propose an adaptive lazy compaction method called ALC for the LSM-tree. The main idea of ALC is to delay compactions operations when the current workload is high, and to perform compactions when the system is low-loaded. With this mechanism, we can lower the impact of compactions on the system's performance and achieve high stability and low latency. Our preliminary experiments show that ALC outperforms RocksDB and the state-of-the-art method SILK in latency and throughput-stability." @default.
- W3138515424 created "2021-03-29" @default.
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- W3138515424 date "2020-12-10" @default.
- W3138515424 modified "2023-09-24" @default.
- W3138515424 title "Adaptive Lazy Compaction with High Stability and Low Latency for Data-Intensive Systems" @default.
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- W3138515424 doi "https://doi.org/10.1109/bigdata50022.2020.9378222" @default.
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