Abstract
This article describes a proposal for the participation of supercomputing platforms and datacenters in the electric market, by implementing demand response techniques and ancillary services. Supercomputing and datacenters are appropriate candidates to adjust their power consumption in order to help the electric network to fulfill specific goals, either by consuming available surplus of energy to execute complex tasks, or by deferring activities when energy is more expensive or generation is lower than normal. Their thermal/cooling infrastructures demand about half of the energy consumption and provide a large inertia that can be carefully used to interact with the power grid. These strategies allow implementing a smart management of the electric grid, achieving a rational utilization of renewable energy sources, and the correct utilization of information technologies to improve decision-making processes. A specific case study is presented: The National Supercomputing Center in Uruguay (Cluster-UY), for which strategies for optimal planning of the execution of tasks and energy utilization are proposed, taking into account the energy consumption, the Quality of Service provided to the users, and the thermal/cooling demands of the infrastructure. In addition, the business opportunities and business models for supercomputing and datacenters in the electric market are revisited. Results suggest the effectiveness of the proposed strategies to implement demand response techniques and provide ancillary services under the smart grid paradigm.
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References
Beloglazov, A., Buyya, R., Choon Lee, Y., Zomaya, A.: A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv. Comput. 82, 47–111 (2010)
Chen, F., Grundy, J., Yang, Y., Schneider, J.G., He, Q.: Experimental analysis of task-based energy consumption in cloud computing systems. In: 4th ACM/SPEC International Conference on Performance Engineering, pp. 295–306 (2013)
Chen, L., Li, N., Low, S., Doyle, J.: Two market models for demand response in power networks. In: First IEEE International Conference on Smart Grid Communications (2010)
Chen, N., Ren, X., Ren, S., Wierman, A.: Greening multi-tenant data center demand response. Perform. Eval. 91, 229–254 (2015)
Chen, T., Marques, A., Giannakis, G.: DGLB: distributed stochastic geographical load balancing over cloud networks. IEEE Trans. Parallel Distrib. Syst. 7, 1866–1880 (2017)
DallAnese, E., Baker, K., Summers, T.: Chance-constrained ac optimal power flow for distribution systems with renewables. IEEE Trans. Power Syst. 32(5), 3427–3438 (2017)
Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A.Y., Talbi, E.G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain. Comput.: Inf. Syst. 4(4), 252–261 (2014)
Du Bois, K., Schaeps, T., Polfliet, S., Ryckbosch, F., Eeckhout, L.: Sweep: evaluating computer system energy efficiency using synthetic workloads. In: 6th International Conference on High Performance and Embedded Architectures and Compilers, pp. 159–166 (2011)
Gonze, X., et al.: ABINIT: first-principles approach to material and nanosystem properties. Comput. Phys. Commun. 180(12), 2582–2615 (2009)
Iturriaga, S., Dorronsoro, B., Nesmachnow, S.: Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters. Int. Trans. Oper. Res. 24(1–2), 199–228 (2017)
Iturriaga, S., Nesmachnow, S.: Scheduling energy efficient data centers using renewable energy. Electronics 5(4), 71 (2016)
Iturriaga, S., García, S., Nesmachnow, S.: An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty. In: Hernández, G., et al. (eds.) CARLA 2014. CCIS, vol. 485, pp. 143–157. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45483-1_11
Jie, B., Tsuji, T.: An analysis of market mechanism and bidding strategy for power balancing market in micro-grid. In: China International Conference on Electricity Distribution (2016)
Johari, R., Tsitsiklis, J.N.: Parameterized supply function bidding: equilibrium and efficiency. Oper. Res. 59(5), 1079–1089 (2011)
Klemper, P.D., Meyer, M.A.: Supply function equilibria in oligopoly under uncertainty. Econometrica 57(6), 1243–1277 (1989)
Kopytov, A.: Sysbench repository. https://github.com/akopytov/sysbench. Accessed Jan 2019
Kurowski, K., Oleksiak, A., Piatek, W., Piontek, T., Przybyszewski, A., Weglarz, J.: Dcworms–a tool for simulation of energy efficiency in distributed computing infrastructures. Simul. Model. Pract. Theory 39, 135–151 (2013)
Momoh, J.: Smart Grid: Fundamentals of Design and Analysis. Wiley-IEEE Press (2012)
Montes de Oca, S., Belzarena, P., Monzon, P.: Optimal demand response in distribution networks with several energy retail companies. In: IEEE Multi-Conference on Systems and Control, pp. 1092–1097 (2016)
Muraña, J., Nesmachnow, S., Armenta, F., Tchernykh, A.: Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores. Cluster Computing (2019). https://doi.org/10.1007/s10586-018-2882-8. Accessed Jan 2019
Nesmachnow, S.: An overview of metaheuristics: accurate and efficient methods for optimisation. Int. J. Metaheuristics 3(4), 320–347 (2014)
Nesmachnow, S., Iturriaga, S.: Cluster-UY: scientific HPC in Uruguay. In: International Supercomputing in México, pp. 1–15 (2019)
Nesmachnow, S., Perfumo, C., Goiri, Í.: Multiobjective energy-aware datacenter planning accounting for power consumption profiles. In: Hernández, G., et al. (eds.) CARLA 2014. CCIS, vol. 485, pp. 128–142. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45483-1_10
Nesmachnow, S., Perfumo, C., Goiri, Í.: Holistic multiobjective planning of datacenters powered by renewable energy. Cluster Comput. 18(4), 1379–1397 (2015)
Phillips, J., et al.: Scalable molecular dynamics with namd. J. Comput. Chem. 26(16), 1781–1802 (2005)
Rockafellar, R., Uryasev, S.: Optimization of conditional value-at-risk. J. Risk 2, 21–42 (2000)
Rong, H., Zhang, H., Xiao, S., Li, C., Hu, C.: Optimizing energy consumption for data centers. Renew. Sustain. Energy Rev. 58, 674–691 (2016)
Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Conference on Power Aware Computing and Systems, pp. 1–5 (2008)
Treibig, J., Hager, G., Wellein, G.: LIKWID: a lightweight performance-oriented tool suite for x86 multicore environments. In: 39th International Conference on Parallel Processing Workshops, pp. 207–216 (2010)
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Nesmachnow, S. et al. (2019). Demand Response and Ancillary Services for Supercomputing and Datacenters. In: Torres, M., Klapp, J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-38043-4_17
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