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Energy-efficient multi-job scheduling model for cloud computing and its genetic algorithm. (English) Zbl 1264.90094
Summary: For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job scheduling model based on Google’s massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals and construct an overall energy efficiency function of the servers as the fitness value of each individual. Meanwhile, in order to accelerate the convergent speed of our algorithm and enhance its searching ability, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient.

MSC:
90B35 Deterministic scheduling theory in operations research
90C59 Approximation methods and heuristics in mathematical programming
Software:
MapReduce
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[1] P. Mell and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology, vol. 53, no. 6, 2009.
[2] J. Hamilton, “Cooperative expendable micro-slice servers (CEMS): low cost, low power servers for internet-scale services,” Citeseer.
[3] C. Belady, “The green grid data center power efficiency metrics: PUE and DCiE,” White paper: Metrics & Measurements, 2007.
[4] ENERGY STAR, “Report to congress on server and data center energy efficiency public law 109-431,” Public law,109:431, 2007.
[5] “Efficiency measurements [EB/OL],” http://www.google.com/about/datacenters/inside/efficiency/power-usage.html.
[6] A. Beloglazov and R. Buyya, “Energy efficient allocation of virtual machines in cloud data centers,” in Proceedings of the 10th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (CCGrid ’10), pp. 577-578, Melbourne, Australia, May 2010. · doi:10.1109/ccgrid.2010.45
[7] A. Berl, E. Gelenbe, M. Di Girolamo et al., “Energy-efficient cloud computing,” Computer Journal, vol. 53, no. 7, pp. 1045-1051, 2010. · doi:10.1093/comjnl/bxp080
[8] R. Buyya, A. Beloglazov, and J. Abawajy, “Energy-Efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges,” Distributed, Parallel, and Cluster Computing, http://arxiv.org/abs/1006.0308/, pp. 6-17, 2010.
[9] J. Baliga, R. W. A. Ayre, K. Hinton, and R. S. Tucker, “Green cloud computing: balancing energy in processing, storage and transport,” Proceedings of the IEEE, vol. 99, no. 1, pp. 149-167, 2011. · doi:10.1109/JPROC.2010.2060451
[10] L. A. Barroso and U. Hölzle, “The datacenter as a computer: an introduction to the design of warehouse-scale machines,” Synthesis Lectures on Computer Architecture, vol. 4, no. 1, pp. 1-108, 2009. · doi:10.2200/S00193ED1V01Y200905CAC006
[11] “Google’s chiller-less data center,” 2009, http://www.datacenterknowledge.com/.
[12] S. Srikantaiah, A. Kansal, and F. Zhao, “Energy aware consolidation for cloud computing,” in Proceedings of the Conference on Power aware computing and systems, p. 10, USENIX Association, San Diego, Calif, USA, 2008.
[13] J. Dean and S. Ghemawat, “MapReduce: simplified data processing on large clusters,” Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. · Zbl 05394988 · doi:10.1145/1327452.1327492
[14] W. T. Hadoop, The Definitive Guide, O’Reilly Media, Sebastopol, Calif, USA, 2009.
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