Research on Technology of Energy Consumption Optimization in Cloud Computing Platform

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With the shortage of energy and global climate warming, as well as the low-carbon economy and green computing coming, the energy consumption of cloud computing has become a critical issue, and even the economic benefits of cloud computing has been widely discussion. In view of the phenomenon of energy is wasted seriously in cloud computing, the energy optimization techniques in cloud computing platform have been studied and summarized in this paper. The concept, characteristics and development of cloud computing are introduced firstly. And then the existing energy consumption optimization approaches of cloud computing are studied deeply. The opening and closing techniques, dynamic voltage adjustment technology, virtual energy-saving technology and resource scheduling optimization technology are studied deeply. Finally, the contents are summarized and the future is looked forward.

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2467-2470

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January 2015

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] H. Kim, A. Beloglazov, R. Buyya, Power-aware Provisioning of Virtual Machines for Real-time Cloud Services, Concurrency and Computation: Practice and Experience, 23, (2011)1491-1505.

DOI: 10.1002/cpe.1712

Google Scholar

[2] L. B. Goh, S. Veeravalli, Design of Fast and Efficient Energy-Aware Gradient-Based Scheduling Algorithms Heterogeneous Embedded Multiprocessor Systems, IEEE Transactions on Parallel and Distributed Systems, 20, (2009) 1-12.

DOI: 10.1109/tpds.2008.55

Google Scholar

[3] M. N. Mezmaz, Y. Kessaci, A parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems. Journal of Parallel and Distributed Computing, (2011) 1497-1508.

DOI: 10.1016/j.jpdc.2011.04.007

Google Scholar

[4] Y. Fang, F. Wang, A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing. Web Information Systems and Mining, Lecture Notes in Computer Science, 6318, (2010) 271-277.

DOI: 10.1007/978-3-642-16515-3_34

Google Scholar

[5] T. Sandeep, Task Scheduling Optimization for the Cloud Computing Systems, International Journal of Advanced Engineering Science and Technologies, 5, (2011) 111-115.

Google Scholar

[6] B. Urgaonkar, P. Shenoy, Resource Overbooking and Application Profiling in A Shared Internet Hosting Platform, ACM Transactions on Internet Technology. (2009) 1-45.

DOI: 10.1145/1462159.1462160

Google Scholar

[7] F. Travostinoa, P. Daspit, Seamless Live Migration of Virtual Machines Over the MAN/WAN, Future Generation Computer Systems, 22, (2006) 901-907.

DOI: 10.1016/j.future.2006.03.007

Google Scholar

[8] H. Jin, W. Gao, Optimizing the Live Migration of Virtual Machine by CPU Scheduling. Journal of Network and Computer Applications, 34, (2010) 1088-1096.

DOI: 10.1016/j.jnca.2010.06.013

Google Scholar

[9] P. Goldberg, Survey of Virtual Machine Research, IEEE Computer, 6, (1974) 34-45.

Google Scholar

[10] A. Hooper, Green computing, Communications of the ACM, 51, (2008) 11-13.

Google Scholar