Escalonamento de workflows científicos ciente de energia verde em servidores distribuídos
Abstract
Using distributed computing, tasks in a scientific workflow can be scheduled across several servers. The current energy consumption of servers is very high, and much of this energy is generated from non-renewable sources. This study proposes the development of a green energy-aware scheduling algorithm, which will allow tasks to be performed more sustainably.
References
Buyya, R. et al. (2018). A manifesto for future generation cloud computing: Research directions for the next decade. ACM Comput. Surv., 51(5).
Gelaro, R. et al. (2017). The modern-era retrospective analysis for research and applications, version 2 (merra-2). Journal of Climate, 30(14):5419 – 5454.
Gupta, V., Shenoy, P., and Sitaraman, R. K. (2019). Combining renewable solar and open air cooling for greening internet-scale distributed networks. In Proceedings of the Tenth ACM International Conference on Future Energy Systems, e-Energy ’19, page 303–314, New York, NY, USA. Association for Computing Machinery.
Message Passing Interface Forum (2021). MPI: A message-passing interface standard version 4.0.
Niaz, H., Shams, M. H., Zarei, M., and Liu, J. J. (2022). Leveraging renewable oversupply using a chance-constrained optimization approach for a sustainable datacenter and hydrogen refueling station: Case study of california. Journal of Power Sources, 540:231558.
Oda, R., Cordeiro, D., and Braghetto, K. R. (2018). Dynamic resource provisioning for scientific workflow executions in clouds. In 2018 IEEE International Conference on Services Computing (SCC), pages 291–294.
OpenMP Architecture Review Board (2021). OpenMP application program interface version 5.2.
Pacheco, P. (2011). An Introduction to Parallel Programming. Morgan Kaufmann.
Topcuoglu, H., Hariri, S., and Wu, M.-Y. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3):260–274.
Vasconcelos, M., Cordeiro, D., Costa, G. D., Dufossé, F., Nicod, J.-M., and Rehn-Sonigo, V. (2023). Optimal sizing of a globally distributed low carbon cloud federation. In The 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing. No prelo.
Vasconcelos, M. F. S., Cordeiro, D., and Dufossé, F. (2022). Indirect network impact on the energy consumption in multi-clouds for follow-the-renewables approaches. In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems — SMARTGREENS, pages 44–55. INSTICC, SciTePress.
Yviquel, H., Pereira, M., Francesquini, E., Valarini, G., Leite, G., Rosso, P., Ceccato, R., Cusihualpa, C., Dias, V., Rigo, S., Souza, A., and Araujo, G. (2022). The OpenMP cluster programming model. In Workshop Proceedings of the 51st International Conference on Parallel Processing. ACM.
Gelaro, R. et al. (2017). The modern-era retrospective analysis for research and applications, version 2 (merra-2). Journal of Climate, 30(14):5419 – 5454.
Gupta, V., Shenoy, P., and Sitaraman, R. K. (2019). Combining renewable solar and open air cooling for greening internet-scale distributed networks. In Proceedings of the Tenth ACM International Conference on Future Energy Systems, e-Energy ’19, page 303–314, New York, NY, USA. Association for Computing Machinery.
Message Passing Interface Forum (2021). MPI: A message-passing interface standard version 4.0.
Niaz, H., Shams, M. H., Zarei, M., and Liu, J. J. (2022). Leveraging renewable oversupply using a chance-constrained optimization approach for a sustainable datacenter and hydrogen refueling station: Case study of california. Journal of Power Sources, 540:231558.
Oda, R., Cordeiro, D., and Braghetto, K. R. (2018). Dynamic resource provisioning for scientific workflow executions in clouds. In 2018 IEEE International Conference on Services Computing (SCC), pages 291–294.
OpenMP Architecture Review Board (2021). OpenMP application program interface version 5.2.
Pacheco, P. (2011). An Introduction to Parallel Programming. Morgan Kaufmann.
Topcuoglu, H., Hariri, S., and Wu, M.-Y. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Transactions on Parallel and Distributed Systems, 13(3):260–274.
Vasconcelos, M., Cordeiro, D., Costa, G. D., Dufossé, F., Nicod, J.-M., and Rehn-Sonigo, V. (2023). Optimal sizing of a globally distributed low carbon cloud federation. In The 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing. No prelo.
Vasconcelos, M. F. S., Cordeiro, D., and Dufossé, F. (2022). Indirect network impact on the energy consumption in multi-clouds for follow-the-renewables approaches. In Proceedings of the 11th International Conference on Smart Cities and Green ICT Systems — SMARTGREENS, pages 44–55. INSTICC, SciTePress.
Yviquel, H., Pereira, M., Francesquini, E., Valarini, G., Leite, G., Rosso, P., Ceccato, R., Cusihualpa, C., Dias, V., Rigo, S., Souza, A., and Araujo, G. (2022). The OpenMP cluster programming model. In Workshop Proceedings of the 51st International Conference on Parallel Processing. ACM.
Published
2023-07-17
How to Cite
ALMEIDA, Guilherme; Ó, Marcelo T. do; FRANCESQUINI, Emilio; CORDEIRO, Daniel.
Escalonamento de workflows científicos ciente de energia verde em servidores distribuídos. In: REGIONAL SCHOOL OF HIGH PERFORMANCE COMPUTING FROM SÃO PAULO (ERAD-SP), 14. , 2023, São José dos Campos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 5-8.
DOI: https://doi.org/10.5753/eradsp.2023.231888.
