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.
You, Xindong; Dong, Chi; Zhou, Li; Huang, Jie; and Jiang, Congfeng
"Anticipation-based Green Data Classification Strategy in Cloud Storage System,"
Applied Mathematics & Information Sciences: Vol. 09:
4, Article 55.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss4/55