A system for adaptive disk rearrangement

Carson, Scott D.

doi:10.1002/spe.4380200302

Cited by 51 publications

(14 citation statements)

References 9 publications

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“…The following studies consider disk layout. The earliest studies of adaptive disk layout are theoretical ones [13] or those based on simulation [14]- [17]. Wong demonstrated that the organ-pipe method, which places the most frequently accessed data in the middle of the disk, was suitable for random disk accesses.…”

Section: Data Placement Controlmentioning

confidence: 99%

Power-Effective File Layout Based on Large Scale Data-Intensive Application in Virtualized Environment

Yagai

Oguchi

Nakano

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn data centers, large numbers of computers are run simultaneously. These computers consume an enormous amount of energy. Several challenges related to this issue have been published. An energyefficient storage management method that cooperates with applications was one effective approach. In this method, data and storage devices are managed using application support and the power consumption of storage devices is significantly decreased. However, existing studies do not take the virtualized environment into account. Recently, many data-intensive applications have been run in a virtualized environment, such as the cloud computing environment. In this paper, we focus on a virtualized environment wherein multiple virtual machines run on a physical computer and a data intensive application runs on each virtual machine. We discuss a method for reducing storage device power consumption using application support. First, we propose two storage management methods using application information. One method optimizes the inter-HDD file layout. This method removes frequently-accessed files from a certain HDD and switches the HDD to power-off mode. To balance loads and reduce seek distances, this method separates a heavily accessed file and consolidates files in a virtual machine with low access frequency. The other method optimizes the intra-HDD file layout, in addition to performing inter-HDD optimization. This method places frequently accessed files near each other. Second, we present our experimental results and demonstrate that the proposed methods can create sufficiently long HDD access intervals that power-off mode can be used, and thereby, reduce the power consumption of storage devices.

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Section: Data Placement Controlmentioning

confidence: 99%

Power-Effective File Layout Based on Large Scale Data-Intensive Application in Virtualized Environment

Yagai

Oguchi

Nakano

et al. 2017

IEICE Trans. Inf. & Syst.

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“…An example is to identify data items -blocks [1,3,43], cylinders [53], or files [48,49,54] -that are referenced frequently and to relocate them to be close together. Rearranging small pieces of data was found to be particularly advantageous [1] but in doing so, contiguous data that used to be accessed together could be split up.…”

Section: Background and Related Workmentioning

confidence: 99%

“…As discussed in Section 2, over the last two decades, there have been several attempts to improve spatial locality in storage systems by clustering together hot data [1,3,43,48,49,53,54]. We refer to these schemes collectively as heat clustering.…”

Section: Heat Clusteringmentioning

confidence: 99%

The automatic improvement of locality in storage systems

Hsu

Smith

Young

2005

ACM Trans. Comput. Syst.

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Disk I/O is increasingly the performance bottleneck in computer systems despite rapidly increasing disk data transfer rates. In this paper, we propose Automatic Locality-Improving Storage (ALIS), an introspective storage system that automatically reorganizes selected disk blocks based on the dynamic reference stream to increase effective storage performance. ALIS is based on the observations that sequential data fetch is far more efficient than random access, that improving seek distances produces only marginal performance improvements, and that the increasingly powerful processors and large memories in storage systems have ample capacity to reorganize the data layout and redirect the accesses so as to take advantage of rapid sequential data transfer. Using trace-driven simulation with a large set of real workloads, we demonstrate that ALIS considerably outperforms prior techniques, improving the average read performance by up to 50% for server workloads and by about 15% for personal computer workloads. We also show that the performance improvement persists as disk technology evolves. Since disk performance in practice is increasing by only about 8% per year [18], the benefit of ALIS may correspond to as much as several years of technological progress.

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“…Finally, some work has been done on block replacement to improve I/O performance [18,19], but are not integrated in an autonomic system as ours.…”

Section: Related Workmentioning

confidence: 99%

Autonomic Storage System Based on Automatic Learning

Hidrobo

Cortes

2004

Lecture Notes in Computer Science

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Abstract. In this paper, we present a system capable of improving the I/O performance in an automatic way. This system is able to learn the behavior of the applications running on top and find the best data placement in the disk in order to improve the I/O performance. This system is built by three independent modules. The first one is able to learn the behavior of a workload in order to be able to reproduce its behavior later on, without a new execution. The second module is a drive modeler that is able to learn how a storage drive works taking it as a "black box". Finally, the third module generates a set of placement alternatives and uses the afore mentioned models to predict the performance each alternative will achieve. We tested the system with five benchmarks and the system was able to find better alternatives in most cases and improve the performance significantly (up to 225%). Most important, the performance predicted where always very accurate (less that 10% error).

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A system for adaptive disk rearrangement

Cited by 51 publications

References 9 publications

Power-Effective File Layout Based on Large Scale Data-Intensive Application in Virtualized Environment

Power-Effective File Layout Based on Large Scale Data-Intensive Application in Virtualized Environment

The automatic improvement of locality in storage systems

Autonomic Storage System Based on Automatic Learning

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