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An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

Li Mao, De Yu Qi, Wei Wei Lin, Bo Liu, Ye Da Li
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 15
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466689992|DOI: 10.4018/IJGHPC.2016040103
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MLA

Mao, Li, et al. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization." IJGHPC vol.8, no.2 2016: pp.43-57. http://doi.org/10.4018/IJGHPC.2016040103

APA

Mao, L., Qi, D. Y., Lin, W. W., Liu, B., & Da Li, Y. (2016). An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization. International Journal of Grid and High Performance Computing (IJGHPC), 8(2), 43-57. http://doi.org/10.4018/IJGHPC.2016040103

Chicago

Mao, Li, et al. "An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization," International Journal of Grid and High Performance Computing (IJGHPC) 8, no.2: 43-57. http://doi.org/10.4018/IJGHPC.2016040103

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Abstract

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

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