FlashDB dynamic self-tuning database for NAND flash

FlashDB is a self-tuning database optimized for sensor networks using NAND flash storage. In practical systems flash is used in different packages such as on-board flash chips, compact flash cards, secure digital cards and related formats. Our experiments reveal non-trivial differences in their acce...

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Veröffentlicht in:Proceedings of the 6th international conference on Information processing in sensor networks S. 410 - 419
Hauptverfasser: Nath, Suman, Kansal, Aman
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 25.04.2007
Schriftenreihe:ACM Conferences
Schlagworte:
Information systems > Data management systems > Database management system engines > Database query processing
Information systems > Information retrieval > Document representation
Information systems > Information retrieval > Search engine architectures and scalability > Search engine indexing
Theory of computation > Theory and algorithms for application domains > Database theory > Database query processing and optimization (theory)
tree
self-tuning index
B
log-structured index
indexing
NAND flash
ISBN:9781595936387, 1595936386
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:FlashDB is a self-tuning database optimized for sensor networks using NAND flash storage. In practical systems flash is used in different packages such as on-board flash chips, compact flash cards, secure digital cards and related formats. Our experiments reveal non-trivial differences in their access costs. Furthermore, databases may be subject to different types of workloads. We show that existing databases for flash are not optimized for all types of flash devices or for all workloads and their performance is thus suboptimal in many practical systems. FlashDB uses a novel self-tuning index that dynamically adapts its storage structure to workload and underlying storage device. We formalize the self-tuning nature of an index as a two-state task system and propose a 3-competitive online algorithm that achieves the theoretical optimum. We also provide a framework to determine the optimal size of an index node that minimizes energy and latency for a given device. Finally, we propose optimizations to further improve the performance of our index. We prototype and compare different indexing schemes on multiple flash devices and workloads, and show that our indexing scheme outperforms existing schemes under all workloads and flash devices we consider.
ISBN:9781595936387
1595936386
DOI:10.1145/1236360.1236412