TRIFL: A Generic Trajectory Index for Flash Storage

Dai Hai Ton That 1 Iulian Sandu Popa 1, 2 Karine Zeitouni 1
2 SMIS - Secured and Mobile Information Systems
PRISM - Parallélisme, Réseaux, Systèmes, Modélisation, UVSQ - Université de Versailles Saint-Quentin-en-Yvelines, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8144
Abstract : Due to several important features, such as high performance, low power consumption, and shock resistance, NAND flash has become a very popular stable storage medium for embedded mobile devices, personal computers, and even enterprise servers. However, the peculiar characteristics of flash memory require redesigning the existing data storage and indexing techniques that were devised for magnetic hard disks. In this article, we propose TRIFL, an efficient and generic TRajectory Index for FLash. TRIFL is designed around the key requirements of trajectory indexing and flash storage. TRIFL is generic in the sense that it is efficient for both simple flash storage devices such as SD cards and more powerful devices such as solid state drives. In addition, TRIFL is supplied with an online self-tuning algorithm that allows adapting the index structure to the workload and the technical specifications of the flash storage device to maximize the index performance. Moreover, TRIFL achieves good performance with relatively low memory requirements, which makes the index appropriate for many application scenarios. The experimental evaluation shows that TRIFL outperforms the representative indexing methods on magnetic disks and flash disks.
Type de document :
Article dans une revue
ACM Transactions on Algorithms, Association for Computing Machinery, 2015, 1 (2), 44 p. 〈10.1145/2786758〉
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https://hal.inria.fr/hal-01176563
Contributeur : Iulian Sandu Popa <>
Soumis le : mercredi 15 juillet 2015 - 15:35:57
Dernière modification le : jeudi 11 janvier 2018 - 06:21:30

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Dai Hai Ton That, Iulian Sandu Popa, Karine Zeitouni. TRIFL: A Generic Trajectory Index for Flash Storage. ACM Transactions on Algorithms, Association for Computing Machinery, 2015, 1 (2), 44 p. 〈10.1145/2786758〉. 〈hal-01176563〉

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