Abstract
Energy efficiency is one of the major concerns when operating datacenters nowadays, as the large amount of energy used by parallel computing infrastructures impacts on both the energy cost and the electricity grid. Power consumption can be lowered by dynamically adjusting the power demand of datacenters, but conflicting objectives such as temperature and quality of service must be taken into account. This paper proposes a multiobjective evolutionary approach to solve the energy-aware scheduling problem in datacenters, regarding power consumption, temperature, and quality of service when controlling servers and cooling infrastructures. Accurate results are reported for both best solutions regarding each of the problem objectives and best trade-off solutions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahmad, I., Ranka, S.: Handbook of Energy-Aware and Green Computing. Chapman & Hall/CRC (2012)
Aikema, D., Simmonds, R., Zareipour, H.: Datacenters in the ancillary services market. In: Int. Green Computing Conf., pp. 1–10 (2012)
Bäck, T., Fogel, D., Michalewicz, Z. (eds.): Handbook of evolutionary computation. Oxford University Press (1997)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary algorithms for solving multi-objective problems. Kluwer, New York (2002)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. J. Wiley & Sons, Chichester (2001)
Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A., Talbi, E.G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sust. Computing (2014)
Ghamkhari, M., Mohsenian-Rad, H.: Data centers to offer ancillary services. In: 3rd Int. Conf. on Smart Grid Communications, pp. 436–441 (2012)
Goiri, I., Katsak, W., Le, K., Nguyen, T., Bianchini, R.: Parasol and GreenSwitch: managing datacenters powered by renewable energy. In: 18th Int. Conf. on Architectural Support for Programming Languages and Operating Systems, pp. 51–64 (2013)
Goiri, I., Le, K., Haque, M., Beauchea, R., Nguyen, T., Guitart, J., Torres, J., Bianchini, R.: GreenSlot: Scheduling energy consumption in green datacenters. In: Int. Conf. for High Performance Computing, Networking, Storage and Analysis (2011)
Goiri, I., Le, K., Nguyen, T., Guitart, J., Torres, J., Bianchini, R.: GreenHadoop: Leveraging green energy in data-processing frameworks. In: 7th European Conf. on Computer Systems, pp. 57–70 (2012)
Iturriaga, S., Nesmachnow, S., Dorronsoro, B., Bouvry, P.: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Computing and Informatics Journal 32(2), 273–294 (2013)
Khan, S., Ahmad, I.: A cooperative game theoretical technique for joint optimization of energy consumption and response time in computational grids. IEEE Trans. Parallel Distrib. Syst. 20, 346–360 (2009)
Kim, J.K., Siegel, H., Maciejewski, A., Eigenmann, R.: Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans. Parallel Distrib. Syst. 19, 1445–1457 (2008)
Koomey, J.: Growth in data center electricity use 2005–2010. Analytic Press (2011)
Krioukov, A., Alspaugh, S., Mohan, P., Dawson, S., Culler, D., Katz, R.: Design and evaluation of an energy agile computing cluster. Tech. Rep. UCB/EECS-2012-13, University of California, Berkeley (2012)
Le, K., Bianchini, R., Zhang, J., Jaluria, Y., Meng, J., Nguyen, T.: Reducing electricity cost through virtual machine placement in high performance computing clouds. In: Int. Conf. for High Performance Computing, Networking, Storage and Analysis (2011)
Lee, Y., Zomaya, A.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22, 1374–1381 (2011)
Lennart, L.: System identification: theory for the user (1999)
Li, Y., Liu, Y., Qian, D.: A heuristic energy-aware scheduling algorithm for heterogeneous clusters. In: 15th Int. Conf. on Parallel and Distributed Systems, pp. 407–413 (2009)
Lindberg, P., Leingang, J., Lysaker, D., Khan, S., Li, J.: Comparison and analysis of eight scheduling heuristics for the optimization of energy consumption and makespan in large-scale distributed systems. The Journal of Supercomputing 59(1), 323–360 (2012)
Liu, Z., Chen, Y., Bash, C., Wierman, A., Gmach, D., Wang, Z., Marwah, M., Hyser, C.: Renewable and cooling aware workload management for sustainable data centers. Performance Evaluation Review 40, 175–186 (2012)
Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y., Talbi, E., Zomaya, A., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. Journal Parallel Distribed Computation 71, 1497–1508 (2011)
Nesmachnow, S.: Computación científica de alto desempeño en la Facultad de Ingeniería, Universidad de la República. Revista de la Asociación de Ingenieros del Uruguay 61, 12–15 (2010)
Nesmachnow, S., Dorronsoro, B., Pecero, J.E., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. Journal of Grid Computing 11(4), 653–680 (2013)
Nesmachnow, S., Perfumo, C., Goiri, I.: Controlling datacenter power consumption while maintaining temperature and QoS levels. In: 3rd IEEE Int. Conf. on Cloud Networking (2014)
Pinel, F., Dorronsoro, B., Pecero, J., Bouvry, P., Khan, S.: A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. Cluster Computing 16(3), 421–433 (2013)
Wang, R., Kandasamy, N., Nwankpa, C., Kaeli, D.R.: Datacenters as controllable load resources in the electricity market. In: IEEE 33rd Int. Conf. on Distributed Computing Systems, pp. 176–185 (2013)
Zomaya, A.Y., Lee, Y.C.: Energy Efficient Distributed Computing Systems. Wiley-IEEE Computer Society Press (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nesmachnow, S., Perfumo, C., Goiri, Í. (2014). Multiobjective Energy-Aware Datacenter Planning Accounting for Power Consumption Profiles. In: Hernández, G., et al. High Performance Computing. CARLA 2014. Communications in Computer and Information Science, vol 485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45483-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-662-45483-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45482-4
Online ISBN: 978-3-662-45483-1
eBook Packages: Computer ScienceComputer Science (R0)