Abstract
This work addresses the problem of high energy consumption and carbon emissions by data centers which support the traditional computing style. In order to overcome this problem we consider two allocation scenarios: single allocation and global optimization of available resources and propose the optimization algorithms. The main idea of these algorithms is to find a server in the data center with the lowest energy consumption and/or carbon emission based on current status of data center and service level agreement requirements, and move the workload there. The optimization algorithms are devised based on Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE). The simulation results demonstrate that the proposed algorithms enable the saving in energy consumption from 10% to 31% and in carbon emission from 10% to 87%.
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References
Dang, M.-Q., Basmadjian, R., De Meer, H., Lent, R., Mahmoodi, T., Sannelli, D., Mezza, F., Dupont, C.: Energy efficient resource allocation strategy for cloud data centres. In: 26th Int. Symposium on Computer and Information Sciences, pp. 133–141. Springer Press (2011)
Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.-Q., Pentikousis, K.: Energy-Efficient Cloud Computing. J. Computer 53(7), 1045–1051 (2010)
Bradley, D.J., Harper, R.E., Hunter, S.W.: Workload-based power management for parallel computer systems. IBM J. of Research and Development 47(5-6), 703–718 (2003)
Meisner, D., Gold, B.T., Wenisch, T.F.: PowerNap: Eliminating server idle power. In: 14th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 205–216. ACM Press (2009)
Carrol, R., Balasubramaniam, S., Donnelly, W.D.: Dynamic optimization solution for green service migration in data centres. In: IEEE International Conference on Communications, pp. 1–6. IEEE Press (2011)
Barbagallo, D., Nitto, E., Dubois, D. J., Mirandola, R.: A Bio-inspired algorithm for energy optimization in a self-organizing data center. In: 1st International Conference on Self-organizing Architectures, pp.127-151, Springer press, 2010.
Berral, J.L., Goiri, I., Nou, R., Julia, F., Guitart, J., Gavalda, R., Torres, J.: Towards energy-aware scheduling in data centers using machine learning. In: 1st International Conference on Energy-Efficient Computing and Networking, pp. 215–224. ACM Press (2010)
Tang, Q., Gupta, S.K.S., Varsamopoulos, G.: Energy-efficient thermal-aware task scheduling for homogemeous high-performance computing data centers: a cyber-physical approach. IEEE Transactions on Parallel and Distributed Systems 19(11), 1458–1472 (2008)
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Quan, D.M., Somov, A., Dupont, C. (2012). Energy Usage and Carbon Emission Optimization Mechanism for Federated Data Centers. In: Huusko, J., de Meer, H., Klingert, S., Somov, A. (eds) Energy Efficient Data Centers. E2DC 2012. Lecture Notes in Computer Science, vol 7396. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33645-4_12
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DOI: https://doi.org/10.1007/978-3-642-33645-4_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33644-7
Online ISBN: 978-3-642-33645-4
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