Sobre a simulação de algoritmos de escalonamento cientes de energia renovável
Resumo
A alta demanda por serviços de computação em nuvem é preocupante devido aos elevados gastos energéticos e o resultante impacto ambiental. Uma possível estratégia para reduzir esses impactos é empregar algoritmos de escalonamento cientes de energia renovável. No entanto, o custo de testar e avaliar tais algoritmos em infraestruturas reais pode ser muito alto, o que motiva o uso de simuladores. Neste trabalho é apresentada a complexidade na delimitação de um modelo de escalonamento ciente de energia renovável e algumas das opções modernas para simulação de infraestrutura computacional.
Palavras-chave:
Escalonamento e Balanceamento de Carga, Modelagem e Simulação de Arquiteturas e Sistemas Paralelos e Distribuídos, Sistemas Distribuídos, Virtualização de Sistemas
Referências
Buyya, R., Ranjan, R., and Calheiros, R. N. (2009). Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In 2009 International Conference on High Performance Computing Simulation, pages 1-11.
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.
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.
Publicado
07/04/2022
Como Citar
CUPELLI, Artur E. L. e; VASCONCELOS, Miguel F. S.; LIMA, Karla; CORDEIRO, Daniel.
Sobre a simulação de algoritmos de escalonamento cientes de energia renovável. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DE 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.
