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April  2016, 12(2): 667-685. doi: 10.3934/jimo.2016.12.667

Effect of energy-saving server scheduling on power consumption for large-scale data centers

1. 

Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan

2. 

Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192

Received  October 2014 Revised  March 2015 Published  June 2015

Large-scale data centers for cloud computing services consist of a number of commodity servers, resulting in a huge amount of power consumption. In order to save power consumption, BEEMR (Berkeley Energy Efficient MapReduce), a MapReduce workload manager, is proposed. In a BEEMR-based data center, servers are allocated to either of the interactive and batch zones. Arriving jobs of a small size begin to be processed immediately in the interactive zone, while large-sized jobs are queued and served simultaneously at every fixed service period in the batch zone. In this paper, we analyze the performance of BEEMR-type job scheduling. We consider two queueing models for the interactive and batch zones. The interactive zone is modeled as a single-server queueing system with processor-sharing (PS) service. In terms of the batch zone, we consider a queueing system with gated service in which arriving jobs are queued and begin to be served when a fixed service period starts. For these models, the time-average power consumption and the mean response time are derived. Numerical examples show that the power consumption is significantly affected by the allocation of servers to both zones, while the power consumption is insensitive to the length of the batch-service period.
Citation: Masataka Kato, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. Effect of energy-saving server scheduling on power consumption for large-scale data centers. Journal of Industrial & Management Optimization, 2016, 12 (2) : 667-685. doi: 10.3934/jimo.2016.12.667
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show all references

References:
[1]

$2^{nd}$ edition, Springer, New York, 2003.  Google Scholar

[2]

in Proc. IEEE INFOCOM'05, (2005), 784-795. doi: 10.1109/INFCOM.2005.1498310.  Google Scholar

[3]

Morgan & Claypool, California, 2009. doi: 10.2200/S00193ED1V01Y200905CAC006.  Google Scholar

[4]

Springer, New York, 1999. doi: 10.1007/978-1-4757-3124-8.  Google Scholar

[5]

Springer, New York, 2005.  Google Scholar

[6]

Lawrence Berkeley National Laboratory, LBNL-363E, 2007. Google Scholar

[7]

in Proc. The European Professional Society on Computer Systems 2012, (2012), 43-56. doi: 10.1145/2168836.2168842.  Google Scholar

[8]

Stochastic Processes and their Applications, 24 (1987), 287-292. doi: 10.1016/0304-4149(87)90019-6.  Google Scholar

[9]

H. Masuyama, Error bounds for augmented truncations of discrete-time block-monotone Markov chains under geometric drift conditions,, Accepted for publication in Advances in Applied Probability, ().  doi: 10.1239/aap/1427814582.  Google Scholar

[10]

in Proc. Workshop on Energy-Efficient Design 2009, (2009). Google Scholar

[11]

Operations Research, 19 (1971), 371-385. Google Scholar

[12]

in Proc. International Conference on Information Networking 2012, (2012), 70-75. doi: 10.1109/ICOIN.2012.6164352.  Google Scholar

[13]

Journal of Applied Probability, 35 (1998), 517-536. doi: 10.1239/jap/1032265201.  Google Scholar

[14]

Prentice-hall, Englewood Cliffs, NJ, 1989.  Google Scholar

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