We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.
Koji HASEBE
University of Tsukuba
Jumpei OKOSHI
Hitachi, Ltd.
Kazuhiko KATO
University of Tsukuba
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Koji HASEBE, Jumpei OKOSHI, Kazuhiko KATO, "Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 10, pp. 1744-1754, October 2015, doi: 10.1587/transinf.2014EDP7421.
Abstract: We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7421/_p
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@ARTICLE{e98-d_10_1744,
author={Koji HASEBE, Jumpei OKOSHI, Kazuhiko KATO, },
journal={IEICE TRANSACTIONS on Information},
title={Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction},
year={2015},
volume={E98-D},
number={10},
pages={1744-1754},
abstract={We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.},
keywords={},
doi={10.1587/transinf.2014EDP7421},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 1744
EP - 1754
AU - Koji HASEBE
AU - Jumpei OKOSHI
AU - Kazuhiko KATO
PY - 2015
DO - 10.1587/transinf.2014EDP7421
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2015
AB - We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.
ER -