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key-value Store Coupled with an Operating System for Storing Large-Scale values

Computers, Materials & Continua 2022年第11期73卷 3333-3350页

作者： Jeonghwan Im Hyuk-Yoon Kwon Graduate School of Data Science Seoul National University of Science and TechnologySeoulKorea Department of Industrial Engineering Graduate School of Data ScienceResearch Center for Electrical and Information ScienceSeoul National University of Science and TechnologySeoulKorea

The key-value store can provide flexibility of data types because it does not need to specify the data types to be stored in advance and can store any types of data as the value of the key-value *** types of studies have been conducted to improve the performance of the key-value store while maintaining its ***,the research efforts storing the large-scale values such as multimedia data files(e.g.,images or videos)in the key-value store were *** this study,we propose a new key-value store,WR-Store++aiming to store the large-scale values ***,it provides a new design of separating data and index by working with the built-in data structure of the Windows operating system and the file *** utilization of the built-in data structure of the Windows operating system achieves the efficiency of the key-value store and that of the file system extends the limited space of the storage *** also present chunk-based memory management and parallel processing of WR-Store++to further improve its performance in the GET *** the experiments,we show that WR-Store++can store at least 32.74 times larger datasets than the existing baseline key-value store,WR-Store,which has the limitation in storing large-scale data ***,in terms of processing efficiency,we show that WR-Store++outperforms not only WR-Store but also the other state-ofthe-art key-value stores,LevelDB,RocksDB,and BerkeleyDB,for individual key-value operations and mixed workloads.

关键词： key-value stores large-scale values chunk-based memory management parallel processing

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