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Anticipation-based Green Data Classification Strategy in Cloud Storage System |
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PP: 2151-2160 |
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Author(s) |
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Xindong You,
Chi Dong,
Li Zhou,
Jie Huang,
Congfeng Jiang,
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Abstract |
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| The energy consumption problem is one of the critical issues to be addressed in current large-scale storage systems. In order
to reduce energy consumption of cloud storage system and meet the performance requirements of users, this paper proposes a green
data classification strategy based on anticipation (AGDC), which classify the data in cloud storage system: the hot data stored in the
hot disk regions, the cold data stored in the cold disk regions. AGDC employ neural-network prediction on seasonal data, prediting
the temperature of data in the next period, executing seasonal data migration in cold and hot regions. This paper also adopts a new
correlating algorithm on new data, analyzes its relations with old data in the storage system and prediting the data temperature. New
energy consumption model also established in this paper. Simulation experiments based on Gridsim showed that the cloud storage
system with green data classification strategy based on anticipation has a good effect on reducing energy consumption. At the expense
of average response time of 0.005s, proposed algorithm saved about 16% of energy consumption compared TDCS. |
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