An Access Prefetching Strategy for Accessing Small Files Based on Swift

Zhu, Dongjie; Du, Hongmei; Qiao, Xueming; Liu, Cheng‐Lin; Kong, Liang; Li, Aiguo; Fu, Zhongchuan

doi:10.1016/j.procs.2018.04.269

Cited by 6 publications

(5 citation statements)

References 5 publications

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“…When the data are accessed, the model prefetches the associated data and puts them in the cache according to a predetermined prefetching strategy, such as the strategies described by Wildani 21 and Zhu. 12 The method reported by Zhu calculates the access distance between data whose access times differ by less than 10%, and the authors define the distance according to the average access interval between data. This method explicitly classifies the data but does not consider the spatial locality and the effect of the data size on the content of the cache.…”

Section: Data Correlation Mining Algorithmmentioning

confidence: 99%

“…The other approach is an offline association mining algorithm that calculates the correlations among data periodically. When the data are accessed, the model prefetches the associated data and puts them in the cache according to a predetermined prefetching strategy, such as the strategies described by Wildani 21 and Zhu 12 . The method reported by Zhu calculates the access distance between data whose access times differ by less than 10%, and the authors define the distance according to the average access interval between data.…”

Section: Related Studiesmentioning

confidence: 99%

“…The cache prefetching technology has been reported to effectively reduce the data access latency, thereby improving the concurrency of the storage system 9,10 . These strategies have produced good results and are widely used in storage efficiency optimization, 11–13 data deduplication 14,15 and other fields. Data grouping is a strategy that merges related data into mutually exclusive groups and accesses them by group, which effectively reduces the disk I/O of the storage system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A data grouping model based on cache transaction for unstructured data storage systems

Zhu

Sun

et al. 2021

Int J of Intelligent Sys

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Cache prefetching technology has become the mainstream data access optimization strategy in the Industrial Intelligent Systems (IIS) and the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships. Therefore, researchers have to make a choice between spatial locality and temporal locality to ensure an acceptable computational complexity. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency.Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art and traditional methods, our algorithm achieves an average increase in the cache hit rate of 5%-20% on the MSR, VDI-LUN, and KC

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Section: Data Correlation Mining Algorithmmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A data grouping model based on cache transaction for unstructured data storage systems

Zhu

Sun

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…The other approach is an offline association mining algorithm that calculates the correlations among data in advance. When the data are accessed, the model prefetches the associated data and puts them in the cache according to a predetermined prefetching strategy, such as the strategies described by Wildani [20] and Zhu [12]. The method reported by Zhu calculates the access distance between data whose access times differ by less than 10%, and the authors define the distance according to the average access interval between data.…”

Section: Data Correlation Mining Algorithmmentioning

confidence: 99%

“…The cache prefetching technology has been reported to effectively reduce the data access latency, thereby improving the concurrency of the storage system [9], [10]. These strategies have produced good results and are widely used in storage efficiency optimization [11], [12], [13], data deduplication [14], [15] and other fields. Data grouping is a strategy that merges related data into mutually exclusive groups and accesses them by group, which effectively reduces the disk I/O of the storage system.…”

Section: Introductionmentioning

confidence: 99%

CTDGM: A Data Grouping Model Based on Cache Transaction for Unstructured Data Storage Systems

Zhu,

Du,

Sun

et al. 2020

Preprint

Self Cite

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Cache prefetching technology has become the mainstream data access optimization strategy in the data centers. However, the rapidly increasing of unstructured data generates massive pairwise access relationships, which can result in a heavy computational burden for the existing prefetching model and lead to severe degradation in the performance of data access. We propose cache-transaction-based data grouping model (CTDGM) to solve the problems described above by optimizing the feature representation method and grouping efficiency. First, we provide the definition of the cache transaction and propose the method for extracting the cache transaction feature (CTF). Second, we design a data chunking algorithm based on CTF and spatiotemporal locality to optimize the relationship calculation efficiency. Third, we propose CTDGM by constructing a relation graph that groups data into independent groups according to the strength of the data access relation. Based on the results of the experiment, compared with the state-of-the-art methods, our algorithm achieves an average increase in the cache hit rate of 12% on the MSR dataset with small cache size (0.001% of all the data), which in turn reduces the number of data I/O accesses by 50% when the cache size is less than 0.008% of all the data.

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SP-TSRM: A Data Grouping Strategy in Distributed Storage System

Zhu

Cao

et al. 2018

Algorithms and Architectures for Parallel Processing

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An Access Prefetching Strategy for Accessing Small Files Based on Swift

Cited by 6 publications

References 5 publications

A data grouping model based on cache transaction for unstructured data storage systems

A data grouping model based on cache transaction for unstructured data storage systems

CTDGM: A Data Grouping Model Based on Cache Transaction for Unstructured Data Storage Systems

SP-TSRM: A Data Grouping Strategy in Distributed Storage System

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