About renewable energy-aware scheduling algorithms simulation
Abstract
The high demand for cloud computing services is worrying due to the elevated energy consumption and the resulting environmental impact. One possible strategy to deal with these impacts is the usage of renewable energy-aware scheduling algorithms. Nevertheless, the cost of testing and assessing such algorithms can be prohibitive in real platforms, which motivates the adoption of simulators. This paper presents the complexity of delimiting renewable energy-aware models and modern options to simulate computational infrastructures.
Keywords:
Scheduling and Load Balancing, Modeling and Simulation of Parallel and Distributed Systems and Architectures, Distributed Systems, System Virtualization
References
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Chaurasia, N., Kumar, M., Chaudhry, R., and Verma, O. P. (2021). Comprehensive survey on energy-aware server consolidation techniques in cloud computing. The Journal of Supercomputing, pages 1-56.
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Heinrich, F. C., Cornebize, T., Degomme, A., Legrand, A., Carpen-Amarie, A., Hunold, S., Orgerie, A.-C., and Quinson, M. (2017). Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node. In 2017 IEEE International Conference on Cluster Computing (CLUSTER), pages 92-102.
IEA (2021). Data centres and data transmission networks, https://www.iea.org/reports/data-centres-and-data-transmission-networks, Novembro.
Casanova, H., Ferreira da Silva, R., Tanaka, R., Pandey, S., Jethwani, G., Koch, W., Albrecht, S., Oeth, J., and Suter, F. (2020). Developing Accurate and Scalable Simulators of Production Workflow Management Systems with WRENCH. Future Generation Computer Systems, 112:162-175.
Casanova, H., Giersch, A., Legrand, A., Quinson, M., and Suter, F. (2014). Versatile, scalable, and accurate simulation of distributed applications and platforms. Journal of Parallel and Distributed Computing, 74(10):2899-2917.
Chaurasia, N., Kumar, M., Chaudhry, R., and Verma, O. P. (2021). Comprehensive survey on energy-aware server consolidation techniques in cloud computing. The Journal of Supercomputing, pages 1-56.
Cook, G., Lee, J., Tsai, T., Kong, A., Deans, J., Johnson, B., and Jardim, E. (2017). Clicking clean: who is winning the race to build a green internet. Greenpeace Inc., Washington, DC, 5.
Cuadrado-Cordero, I., Orgerie, A.-C., and Menaud, J.-M. (2017). Comparative experimental analysis of the quality-of-service and energy-efficiency of VMs and containers' consolidation for cloud applications. In 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1-6.
Dutot, P.-F., Mercier, M., Poquet, M., and Richard, O. (2015). Batsim: a realistic language-independent resources and jobs management systems simulator. In Job Scheduling Strategies for Parallel Processing, pages 178-197. Springer.
Fieni, G., Rouvoy, R., and Seiturier, L. (2021). Selfwatts: On-the-fly selection of performance events to optimize software-defined power meters. In 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pages 324-333.
Heinrich, F. C., Cornebize, T., Degomme, A., Legrand, A., Carpen-Amarie, A., Hunold, S., Orgerie, A.-C., and Quinson, M. (2017). Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node. In 2017 IEEE International Conference on Cluster Computing (CLUSTER), pages 92-102.
IEA (2021). Data centres and data transmission networks, https://www.iea.org/reports/data-centres-and-data-transmission-networks, Novembro.
Published
2022-04-07
How to Cite
CUPELLI, Artur E. L. e; VASCONCELOS, Miguel F. S.; LIMA, Karla; CORDEIRO, Daniel.
About renewable energy-aware scheduling algorithms simulation. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 13. , 2022, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 9-12.
DOI: https://doi.org/10.5753/eradsp.2022.222121.
