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Skewly replicating hot data to construct a power-efficient storage cluster

Zhang, Lingwei, Deng, Yuhui, Zhu, Weiheng, Zhou, Jipeng, Wang, Frank Z. (2015) Skewly replicating hot data to construct a power-efficient storage cluster. Journal of Network and Computer Applications (IF=2.762, ranked A at ERA), 50 . pp. 168-179. (doi:10.1016/j.jnca.2014.06.005) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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http://dx.doi.org/10.1016/j.jnca.2014.06.005

Abstract

The exponential data growth is presenting challenges to traditional storage systems. Component-based cluster storage systems, due to their high scalability, are becoming the architecture of next generation storage systems. Cluster storage systems often use data replication to ensure high availability, fault tolerance, and load balance. However, this kind of data replication not only consumes a large amount of storage resources, but also generates more energy consumption. This paper presents a power-aware data replication strategy by leveraging data access behavior. This strategy uses 80/20 rule (80% of the data accesses often go to 20% of the storage space) to skewly replicate only a small amount of frequently accessed data. Furthermore, the storage nodes are divided into a hot node set and a cold node set. Hot nodes, which store a small amount of hot data copies, are always in an active state to guarantee the QoS of the system. The cold nodes which store a large volume of infrequently accessed cold data are placed in a low-power state, thus reducing the energy consumption of the cluster storage system. Simulation results show that the proposed strategy can effectively reduce the resource and energy consumption of the system, while ensuring system performance.

Item Type: Article
DOI/Identification number: 10.1016/j.jnca.2014.06.005
Uncontrolled keywords: Big Data, Green Computing
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Computing
Depositing User: Frank Wang
Date Deposited: 04 Nov 2014 20:48 UTC
Last Modified: 06 Feb 2020 04:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/44007 (The current URI for this page, for reference purposes)
Wang, Frank Z.: https://orcid.org/0000-0003-4378-2172
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