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Negotiation Approach for the Participation of Datacenters and Supercomputing Facilities in Smart Electricity Markets

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Abstract

This article presents an approach for the participation of datacenters and supercomputing facilities in smart electricity markets. This is a relevant problem in modern smart grid systems to implement demand response strategies for a better use of resources to guarantee energy efficiency. The proposed approach includes a datacenter model based on empirical information to determine the power consumption of CPU-intensive and memory-intensive tasks. A negotiation approach between the datacenter and its tenants and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling the operation of the National Supercomputing Center in Uruguay. The obtained results indicate that the proposed approach is effective to provide appropriate demand response actions according to monetary incentives. Accurate results are reported for realistic problem instances and different types of tenants.

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REFERENCES

  1. Armenta-Cano, F., Tchernykh, A., Cortes-Mendoza, J., Yahyapour, R., Drozdov, A., Bouvry, P., Kliazovich, D., Avetisyan, A., and Nesmachnow, S., Min_c: heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention, Program. Comput. Software, 2017, vol. 43, no. 3, pp. 204–215.

    Article  MathSciNet  Google Scholar 

  2. Berndt, H., Hermann, M., Kreye, H.D., Reinisch, R., Scherer, U., and Vanzetta, J., Network and System Rules of German Transmission System Operators, 1.02 ed., Association of network operators, 2019.

  3. Calheiros, R., Ranjan, R., Beloglazov, A., De Rose, C., and Buyya, R., CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software: Pract. Exper., 2011, vol. 41, no. 1, pp. 23–50.

    Google Scholar 

  4. PJM Day-Ahead and Real-Time Market Operations Division, Manual 11: Energy & Ancillary Services Market Operations, 108th ed., PJM Interconnection LLC, March 2018.

  5. Delforge, P. and Whitney, J., Scaling up energy efficiency across the data center industry: Evaluating key drivers and barriers, Tech. Rep., Natural Resources Defense Council and Anthesis, 2014.

    Google Scholar 

  6. PJM Dispatch Operations Division, Manual 12: Balancing Operations, 39th ed., PJM Interconnection LLC, Feb. 2019.

  7. Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A.Y., Talbi, E., and Bouvry, P., A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems, Sust. Comput.: Inf. Syst., 2014, vol. 4, no. 4, pp. 252–261.

    Google Scholar 

  8. Du Bois, K., Schaeps, T., Poliet, S., Ryckbosch, F., and Eeckhout, L., SWEEP: evaluating computer system energy efficiency using synthetic workloads, Proc. 6th Int. Conf. on High Performance and Embedded Architectures and Compilers, Heraklion, 2011, pp. 159–166.

  9. Durillo, J.J. and Nebro, A.J., jMetal: a Java framework for multiobjective optimization, Adv. Eng. Software, 2011, vol. 42, pp. 760–771.

    Article  Google Scholar 

  10. European Automotive Research Partner Association, Smart Grids European Technology Platform, Jan. 2020. https://www.etip-snet.eu/.

  11. Federal Energy Regulatory Commission, Assessment of demand response & advanced metering. Tech. Rep., 2006, no. AD-06-2-00.

  12. Feng, X., Ge, R., and Cameron, K.W., Power and energy profiling of scientific applications on distributed systems, Proc. 19th IEEE Int. Parallel and Distributed Processing Symp., Denver, 2005, pp. 34–44.

  13. Grushin, D.A. and Kuzyurin, N.N., On effective scheduling in computing clusters, Program. Comput. Software, 2019, vol. 45, pp. 398–404.

    Article  Google Scholar 

  14. Hsu, C.-H. and Poole, S.W., Power signature analysis of the SPECpower_ssj2008 benchmark, Proc. IEEE Int. Symp. on Performance Analysis of Systems and Software, Austin, TX, 2011, pp. 227–236.

  15. Iturriaga, S., Garcia, S., and Nesmachnow, S., An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty, Commun. Comput. Inf. Sci., 2014, vol. 485, pp. 143–157.

    Google Scholar 

  16. Iturriaga, S. and Nesmachnow, S., Scheduling energy efficient data centers using renewable energy, Electronics, 2016, vol. 5, no. 4, p. 71.

    Article  Google Scholar 

  17. Iturriaga, S., Nesmachnow, S., Goñi, G., Dorronsoro, B., and Tchernykh, A., Evolutionary algorithms for optimizing cost and QoS on cloud-based content distribution networks, Program. Comput. Software, 2019, vol. 45, pp. 544–556.

    Article  Google Scholar 

  18. Johari, R. and Tsitsiklis, J., Parameterized supply function bidding: equilibrium and efficiency, Oper. Res., 2011, vol. 59, no. 5, pp. 1079–1089.

    Article  MathSciNet  Google Scholar 

  19. Klemperer, P. and Meyer, M., Supply function equilibria in oligopoly under uncertainty, Econometrica, 1989, vol. 57, no. 6, pp. 1243–1277.

    Article  MathSciNet  Google Scholar 

  20. Kluyver, T., Ragan-Kelley, B., Perez, F., Granger, B.E., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J.B., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C., et al., Jupyter notebooks – a publishing format for reproducible computational workows, in Proc. 20th Int. Conf. on Electronic Publishing Positioning and Power in Academic Publishing: Players, Agents and Agendas, Loizides, F. and Schmidt, B., Eds., IOS Press, 2016, pp. 87–90.

  21. Kurowski, K., Oleksiak, A., Piatek, W., Piontek, T., Przybyszewski, A., and Weglarz, J., DCworms – a tool for simulation of energy efficiency in distributed computing infrastructures, Simul. Model. Pract. Theory, 2013, vol. 39, pp. 135–151.

    Article  Google Scholar 

  22. McKinney, W., pandas: a foundational Python library for data analysis and statistics, Proc. PyHPC 2011: Python for High Performance and Scientific Computing, Seattle, 2011, pp. 1–9.

  23. Momoh, J., Smart Grid: Fundamentals of Design and Analysis, Wiley-IEEE Press, 2012.

    Book  Google Scholar 

  24. Muraña, J., Nesmachnow, S., Armenta, F., and Tchernykh, A., Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores, Cluster Comput., 2019, vol. 22, no. 3, pp. 839–859.

    Article  Google Scholar 

  25. Muraña, J., Nesmachnow, S., Iturriaga, S., and Tchernykh, A., Power consumption characterization of synthetic benchmarks in multicores, Proc. 4th Latin American Conf. High Performance Computing CARLA 2017, Buenos Aires, 2017, pp. 21–37.

  26. Nesmachnow, S., An overview of metaheuristics: accurate and efficient methods for optimisation, Int. J. Metaheuristics, 2014, vol. 3, no. 4, pp. 320–347.

    Article  Google Scholar 

  27. Nesmachnow, S. and Iturriaga, S., Cluster-UY: scientific HPC in Uruguay, Proc. Int. Supercomputing Conf. in Mexico, Monterrey, 2019, pp. 1–15.

  28. Nesmachnow, S., Perfumo, C., and Goiri, I., Holistic multiobjective planning of datacenters powered by renewable energy, Cluster Comput., 2015, vol. 18, no. 4, pp. 1379–1397.

    Article  Google Scholar 

  29. Chen, N., Ren, X., Ren, S., and Wierman, A., Greening multitenant data center demand response, Perform. Eval., 2015, vol. 91, pp. 229–254.

    Article  Google Scholar 

  30. Montes de Oca, S., Belzarena, P., and Monzón, P., Benefits of optimal demand response in distribution networks in a competitive retail market, Proc. IEEE URUCON, Montevideo, 2017, pp. 1–4.

  31. Montes de Oca, S., Belzarena, P., and Monzón, P., Optimal demand response in distribution networks with several energy retail companies, Proc. IEEE Conf. on Control Applications, Buenos Aires, 2016, pp. 1092–1097.

  32. Oliphant, T.E., Python for scientific computing, Comput. Sci. Eng., 2007, vol. 9, no. 3, pp. 10–20.

    Article  Google Scholar 

  33. Oo, T.Z., Tran, N., Ren, S., and Hong, C., A Survey on Coordinated Power Management in Multi-Tenant Data Centers, 1st ed., Springer, 2018.

    Book  Google Scholar 

  34. Paganini, F., Belzarena, P., and Monzón, P., Decision making in forward power markets with supply and demand uncertainty, Proc. Int. Conf. on Information Sciences and Systems, Auckland, 2014, pp. 1–6.

  35. Parikh, N. and Boyd, S., Proximal algorithms, Found. Trends Optim., 2014, vol. 1, no. 3, pp. 127–239.

    Article  Google Scholar 

  36. National Grid PLC, Product Roadmap for Frequency Response and Reserve, London, 2017.

    Google Scholar 

  37. Stoft, S., Power System Economics: Designing Markets for Electricity, 1st ed., Wiley-IEEE Press, 2002.

    Book  Google Scholar 

  38. Theil, H., Economic Forecasts and Policy, 2nd ed., Amsterdam: North-Holland Publ. Co., 1961.

    Google Scholar 

  39. Tijs, S. and Driessen, T.,Game theory and cost allocation problems, Manag. Sci., 1986, vol. 32, no. 8, pp. 1015–1028.

    Article  MathSciNet  Google Scholar 

  40. Tran, N., Oo, T., Ren, S., Han, Z., Huh, E.-N., and Seon Hong, C., Reward-to-reduce: an incentive mechanism for economic demand response of colocation datacenters, IEEE J. Select. Areas Commun., 2016, vol. 34, no. 12, pp. 3941–3953.

    Article  Google Scholar 

  41. Tran, N., Do, C.T., Ren, S., Han, Z., and Hong, C.S., Incentive Mmechanisms for economic and emergency demand responses of colocation datacenters, IEEE J. Select. Areas Commun., 2015, vol. 33, no. 12, pp. 2892–2905.

    Article  Google Scholar 

  42. Wang, Y., Zhang, F., Chi, C., Ren, S., Liu, F., Wang, R., and Liu, Z., A market-oriented incentive mechanism for emergency demand response in colocation data centers, Sust. Comput.: Inf. Syst., 2019, vol. 22, pp. 13–25.

    Google Scholar 

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ACKNOWLEDGMENTS

The work is partially supported by Agencia Nacional de Investigación e Innovación (FSE_2017_1_144789). The work of S. Nesmachnow and S. Iturriaga has been partly funded by ANII and PEDECIBA, Uruguay. The authors also want to thank the Centro Nacional de Supercomputación (Cluster.Uy).

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Authors and Affiliations

  1. Universidad de la República, 1824-1850, 11200, Montevideo, Departamento de Montevideo, Uruguay

    J. Muraña, S. Nesmachnow, S. Iturriaga, S. Montes de Oca, G. Belcredi & P. Monzón

  2. South Ural State University, 454080, Chelyabinsk, Russia

    S. Nesmachnow, V. Shepelev & A. Tchernykh

  3. CICESE Research Center, Zona Playitas, 22860, Ensenada, B.C., Mexico

    A. Tchernykh

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  1. J. Muraña
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  2. S. Nesmachnow
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  3. S. Iturriaga
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  4. S. Montes de Oca
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  5. G. Belcredi
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  6. P. Monzón
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  8. A. Tchernykh
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Correspondence to J. Muraña, S. Nesmachnow, S. Iturriaga, S. Montes de Oca, G. Belcredi, P. Monzón, V. Shepelev or A. Tchernykh.

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Muraña, J., Nesmachnow, S., Iturriaga, S. et al. Negotiation Approach for the Participation of Datacenters and Supercomputing Facilities in Smart Electricity Markets. Program Comput Soft 46, 636–651 (2020). https://doi.org/10.1134/S0361768820080150

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