2006
DOI: 10.1145/1138041.1138043
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Efficient identification of hot data for flash memory storage systems

Abstract: Hot data identification for flash memory storage systems not only imposes great impacts on flash memory garbage collection but also strongly affects the performance of flash memory access and its lifetime (due to wear-levelling). This research proposes a highly efficient method for on-line hot data identification with limited space requirements. Different from past work, multiple independent hash functions are adopted to reduce the chance of false identification of hot data and to provide predictable and excel… Show more

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Cited by 164 publications
(75 citation statements)
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References 5 publications
(8 reference statements)
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“…In addition, performance improvement through hot and cold data classification is also a typical performance improvement technique [13], [14]. Various policies for hot and cold data separation have also been proposed [1], [2]. Meanwhile, Kim et al propose a buffer management scheme called BPLRU to mitigate GC cost induced by random writes [3].…”
Section: Background and Related Workmentioning
confidence: 99%
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“…In addition, performance improvement through hot and cold data classification is also a typical performance improvement technique [13], [14]. Various policies for hot and cold data separation have also been proposed [1], [2]. Meanwhile, Kim et al propose a buffer management scheme called BPLRU to mitigate GC cost induced by random writes [3].…”
Section: Background and Related Workmentioning
confidence: 99%
“…Performance of the flash storage device is greatly influenced by the efficiency of cleaning operations. In order to minimize cleaning cost, many researches such as hot/cold data classification policies [1], [2] and buffer management policy [3] through pattern analysis of I/O requests have been conducted. However, it is difficult to obtain sufficient performance improvement of storage devices without help of the host system, with only limited information in the storage device.…”
Section: Introductionmentioning
confidence: 99%
“…For capturing frequency, unlike other existing hot data detection schemes, our proposed scheme does not maintain a specific counter for all LBAs; instead, the number of BF can present its frequency information so that it consumes a very small amount of memory (8KB) which corresponds to a half of the state-of-the-art scheme [20]. Recency (as well as the frequency) is another important factor to identify hot data.…”
Section: Addressing Mode Switchesmentioning
confidence: 99%
“…Hot and cold data identification itself, however, is out of scope of this paper. Different algorithms such as the LRU discipline [21], sampling-based approach [22], or multihash function scheme [20] can also substitute for our approach.…”
Section: Addressing Mode Switchesmentioning
confidence: 99%
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