An efficient R-tree implementation over flash-memory storage systems

Wu, Chin-Hsien; Chang, Li-Pin; Kuo, Tei-Wei

doi:10.1145/956676.956679

Cited by 84 publications

(62 citation statements)

References 8 publications

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“…Some authors argue that by implementing flash-aware application-specific data structures, performance and endurance can be improved beyond implementation over a file system or even over a block device. Wu et al [2003aWu et al [ , 2003b proposed flashaware implementations of B-trees and R-trees. Their implementations represent a tree node as an ordered set of small items.…”

Section: Flash-aware Application-specific Data Structuresmentioning

confidence: 99%

“…Partitioning the device at the physical level (physical addresses) adversely affects wear leveling because worn-out units in one partition cannot be swapped with relatively fresh units in the other. The Wu et al [2003aWu et al [ , 2003b implementations partition the virtual block address space so both the tree blocks and file-system blocks are managed by the same blockmapping mechanism. In other words, their data structures are flash-aware, but they operate at the virtual block level not at the physical sector level.…”

Section: Flash-aware Application-specific Data Structuresmentioning

confidence: 99%

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Algorithms and data structures for flash memories

2005

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Flash memory is a type of electrically-erasable programmable read-only memory (EEPROM). Because flash memories are nonvolatile and relatively dense, they are now used to store files and other persistent objects in handheld computers, mobile phones, digital cameras, portable music players, and many other computer systems in which magnetic disks are inappropriate. Flash, like earlier EEPROM devices, suffers from two limitations. First, bits can only be cleared by erasing a large block of memory. Second, each block can only sustain a limited number of erasures, after which it can no longer reliably store data. Due to these limitations, sophisticated data structures and algorithms are required to effectively use flash memories. These algorithms and data structures support efficient not-in-place updates of data, reduce the number of erasures, and level the wear of the blocks in the device. This survey presents these algorithms and data structures, many of which have only been described in patents until now.

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Section: Flash-aware Application-specific Data Structuresmentioning

confidence: 99%

Section: Flash-aware Application-specific Data Structuresmentioning

confidence: 99%

Algorithms and data structures for flash memories

2005

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“…Several other studies have proposed efficient data structures and algorithms for flash storage, including flash-optimized B trees [17], R trees [26], stacks [15], queues [15], and hash tables [28]. These algorithms seek to avoid in-place updates and random writes, but they neither study our sampling problem nor propose anything analogous to the key ideas in this paper: B-FILE, semi-random writes, and a skip-list-based search structure.…”

Section: Related Workmentioning

confidence: 99%

Online maintenance of very large random samples on flash storage

Nath

Gibbons

2008

Proc. VLDB Endow.

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Recent advances in flash media have made it an attractive alternative for data storage in a wide spectrum of computing devices, such as embedded sensors, mobile phones, PDA's, laptops, and even servers. However, flash media has many unique characteristics that make existing data management/analytics algorithms designed for magnetic disks perform poorly with flash storage. For example, while random (page) reads are as fast as sequential reads, random (page) writes and in-place data updates are orders of magnitude slower than sequential writes. In this paper, we consider an important fundamental problem that would seem to be particularly challenging for flash storage: efficiently maintaining a very large (100 MBs or more) random sample of a data stream (e.g., of sensor readings). First, we show that previous algorithms such as reservoir sampling and geometric file are not readily adapted to flash. Second, we propose B-FILE, an energy-efficient abstraction for flash media to store self-expiring items, and show how a B-FILE can be used to efficiently maintain a large sample in flash. Our solution is simple, has a small (RAM) memory footprint, and is designed to cope with flash constraints in order to reduce latency and energy consumption. Third, we provide techniques to maintain biased samples with a B-FILE and to query the large sample stored in a B-FILE for a subsample of an arbitrary size. Finally, we present an evaluation with flash media that shows our techniques are several orders of magnitude faster and more energy-efficient than (flash-friendly versions of) reservoir sampling and geometric file. A key finding of our study, of potential use to many flash algorithms beyond sampling, is that "semi-random" writes (as defined in the paper) on flash cards are over two orders of magnitude faster and more energy-efficient than random writes.

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“…Therefore, a concentrated write instruction will decrease the performance skill of a flash memory. Thus, the critical flaw when establishing B-Tree is that a lot of write instructions occurs in a particular area [3][4][5][6][7].…”

Section: Nand Flash Memory's B+-tree Index Buffer Recovery Methodsmentioning

confidence: 99%