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ClusterFetch: A Lightweight Prefetcher for Intensive Disk Reads.

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Title: ClusterFetch: A Lightweight Prefetcher for Intensive Disk Reads.
Authors: Ryu, Junhee1, Lee, Dongeun2, Shin, Kang G.3, Kang, Kyungtae1
Source: IEEE Transactions on Computers. Feb2018, Vol. 67 Issue 2, p284-290. 7p.
Subjects: Disk access (Computer science), Computer memory management, Computer storage devices, Algorithms, Linux operating systems
Abstract: By overlapping disk accesses with computation-intensive operations, prefetching can reduce delays in launching an application and in loading significant amounts of data while the application is running. The key to effective prefetching is making the tradeoff between the mining accuracy of selecting relevant blocks, and the time to decide those blocks. To address this problem, we propose a new prefetcher called ClusterFetch. In its learning mode, ClusterFetch detects periods of intensive disk accesses by monitoring the speed at which read requests are queued; it re-organizes these reads and locates the file opened by the application just before each such period. During subsequent runs of the same application, ClusterFetch prefetches the data associated with the opening of a “trigger” file. Our experimental results show that ClusterFetch implemented in Linux can reduce the application launch time by up to 41.3 percent and the loading time by up to 38.2 percent, while taking up less than 200 KB of main memory. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Computers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Computers%22">IEEE Transactions on Computers</searchLink>. Feb2018, Vol. 67 Issue 2, p284-290. 7p.
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  Data: <searchLink fieldCode="DE" term="%22Disk+access+%28Computer+science%29%22">Disk access (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+memory+management%22">Computer memory management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+storage+devices%22">Computer storage devices</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Linux+operating+systems%22">Linux operating systems</searchLink>
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  Data: By overlapping disk accesses with computation-intensive operations, prefetching can reduce delays in launching an application and in loading significant amounts of data while the application is running. The key to effective prefetching is making the tradeoff between the mining accuracy of selecting relevant blocks, and the time to decide those blocks. To address this problem, we propose a new prefetcher called ClusterFetch. In its learning mode, ClusterFetch detects periods of intensive disk accesses by monitoring the speed at which read requests are queued; it re-organizes these reads and locates the file opened by the application just before each such period. During subsequent runs of the same application, ClusterFetch prefetches the data associated with the opening of a “trigger” file. Our experimental results show that ClusterFetch implemented in Linux can reduce the application launch time by up to 41.3 percent and the loading time by up to 38.2 percent, while taking up less than 200 KB of main memory. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Computers is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TC.2017.2748939
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      – SubjectFull: Computer memory management
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      – SubjectFull: Computer storage devices
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      – SubjectFull: Algorithms
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      – SubjectFull: Linux operating systems
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              Text: Feb2018
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