Smart grid demand response strategies for datacenters
https://doi.org/10.15514/ISPRAS-2021-33(2)-7
Abstract
This article presents demand response techniques for the participation of datacenters in smart electricity markets under the smart grid paradigm. 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 clients and a heuristic planning method for energy reduction optimization are proposed. The experimental evaluation is performed over realistic problem instances modeling different types of clients. Results indicate that the proposed approach is effective to provide appropriate demand response actions according to monetary incentives.
About the Authors
Jonathan MURAÑA-SILVERAUruguay
M.Sc. in Computer Science, Researcher Assistant
Sergio Enrique NESMACHNOW-CÁNOVAS
Uruguay
Ph.D. in Computer Sciences, Full Professor and Researcher
Santiago Damián ITURRIAGA-FABRA
Uruguay
Ph.D. in Computer Sciences, Adjunct Professor
Sebastián MONTES DE OCA
Uruguay
M.Sc. in Electrical Engineering, Researcher Assistant
Gonzalo BELCREDI
Uruguay
Electrical Engineer, Assistant
Pablo Ariel MONZÓN-RANGELOFF
Uruguay
Ph.D. in Electrical Engineering, Chief of the Systems and Control Department
Vladimir Dmitrievitch SHEPELEV
Russian Federation
PhD, Associate Professor
Andrei Nikolaevitch TCHERNYKH
Mexico
PhD, Full Professor at CICESE
References
1. J. Momoh. Smart Grid: Fundamentals of Design and Analysis. Wiley-IEEE Press, 2012, 232 p.
2. J. Muraña, S. Nesmachnow, S. Iturriaga, S. Montes de Oca, G. Belcredi, P. Monzón, V. Shepelev, A. Tchernykh. Negotiation approach for the participation of datacenters and supercomputing facilities in smart electricity markets. Programming and Computer Software, vol. 46, no. 8, 2020, pp. 636–651.
3. N. Parikh and S. Boyd. Proximal Algorithms. In: Foundations and Trends in Optimization, vol. 1, issue 3, 2014, pp. 127-239.
4. J. Muraña, S. Nesmachnow, F. Armenta, and A. Tchernykh. Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores. Cluster Computing, vol. 22, no.3, 2019, pp. 839-859.
5. S. Nesmachnow and S. Iturriaga. Cluster-UY: Collaborative Scientific High Performance Computing in Uruguay. Communications in Computer and Information Science, vol. 1151. 2019, pp. 188-202.
Review
For citations:
MURAÑA-SILVERA J., NESMACHNOW-CÁNOVAS S., ITURRIAGA-FABRA S., MONTES DE OCA S., BELCREDI G., MONZÓN-RANGELOFF P., SHEPELEV V.D., TCHERNYKH A.N. Smart grid demand response strategies for datacenters. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2021;33(2):125-136. (In Russ.) https://doi.org/10.15514/ISPRAS-2021-33(2)-7