Uma Abordagem Hierárquica para Escalonamento de Workflows Científicos Executados em Nuvens
Resumo
Os Workflows científicos são utilizados para modelar experimentos baseados em simulação onde cada simulação é composta por diversos programas que possuem dependência de dados entre si. Muitas dessas simulações necessitam executar em ambientes de alto desempenho para terminar em tempo hábil. Executar workflows nesses ambientes envolve escalonar atividades dos workflows em diferentes recursos. Muitos escalonadores tradicionais buscam otimizar apenas o tempo de execução, porém em cenários mais reais existem vários fatores a serem considerados, e.g. consumo de energia e custo financeiro. Algumas abordagens recentes já levam em consideração múltiplos objetivos, porém muitas delas utilizam funções objetivo ponderadas que podem se tornar um problema quando muitos objetivos são considerados. Esse artigo propõe uma abordagem que usa múltiplas funções objetivo de forma hierárquica, onde os múltiplos objetivos são levados em consideração em uma determinada ordem estabelecida pelo cientista. Foram executados experimentos em workflows sintéticos e reais na nuvem da Amazon EC2. Os resultados obtidos apresentaram melhoras no objetivo escolhido para otimização.
Referências
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Ogasawara, E., Dias, J., Oliveira, D., Porto, F., Valduriez, P., and Mattoso, M. (2011). An algebraic approach for data-centric scientific workflows. Proc. of VLDB Endowment, 4(12):1328–1339.
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de Oliveira, D. C. M. (2012). Uma Abordagem de Apoio à Execução Paralela de Workflows Científicos em Nuvens de Computadores. PhD thesis, Universidade Federal do Rio de Janeiro.
Deelman, E., Gannon, D., Shields, M., and Taylor, I. (2009). Workflows and e-science: An overview of workflow system features and capabilities. Future Generation Computer Systems, 25(5):528–540.
Deelman, E., Mehta, G., Singh, G., Su, M.-H., and Vahi, K. (2007). Pegasus: mapping large-scale workflows to distributed resources. In Workflows for e-Science, pages 376–394. Springer.
Duan, R., Prodan, R., and Li, X. (2014). Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds. Cloud Computing, IEEE Transactions on, 2(1):29–42.
Durillo, J. J., Nae, V., and Prodan, R. (2014). Multi-objective energy-efficient workflow scheduling using list-based heuristics. Future Generation Computer Systems, 36:221–236.
Durillo, J. J., Prodan, R., and Barbosa, J. G. (2015). Pareto tradeoff scheduling of workflows on federated commercial clouds. Simulation Modelling Practice and Theory, 58:95–111.
Fard, H. M., Prodan, R., Barrionuevo, J. J. D., and Fahringer, T. (2012). A multi-objective approach for workflow scheduling in heterogeneous environments. In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), pages 300–309. IEEE Computer Society.
Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for computational tasks: A survey. Computing in Science & Engineering, 10(3):11–21.
Garg, S. K., Buyya, R., and Siegel, H. J. (2009). Scheduling parallel applications on utility grids: time and cost trade-off management. In Proceedings of the Thirty-Second Australasian Conference on Computer Science-Volume 91, pages 151–160. Australian Computer Society, Inc.
Hakem, M. and Butelle, F. (2007). Reliability and scheduling on systems subject to failures. In Parallel Processing, 2007. ICPP 2007. International Conference on, pages 38–38. IEEE.
Ocaña, K. A., de Oliveira, D., Ogasawara, E., Dávila, A. M., Lima, A. A., and Mattoso, M. (2011). Sciphy: A cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes. In Advances in Bioinformatics and Computational Biology, pages 66–70. Springer.
Ocana, K. A., Silva, V., De Oliveira, D., and Mattoso, M. (2015). Data analytics in bioinformatics: Data science in practice for genomics analysis workflows. In e-Science (e-Science), 2015 IEEE 11th International Conference on, pages 322–331. IEEE.
Ogasawara, E., Dias, J., Oliveira, D., Porto, F., Valduriez, P., and Mattoso, M. (2011). An algebraic approach for data-centric scientific workflows. Proc. of VLDB Endowment, 4(12):1328–1339.
Oliveira, D., Ogasawara, E., Ocaña, K., Baião, F., and Mattoso, M. (2012). An adaptive parallel execution strategy for cloud-based scientific workflows. Concurrency and Computation: Practice and Experience, 24(13):1531–1550.
Oliveira, L. S. d. and Saramago, S. A. F. P. (2010). Multiobjective optimization techniques applied to engineering problems. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 32:94 – 105.
Pandey, S., Wu, L., Guru, S. M., and Buyya, R. (2010). A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced information networking and applications (AINA), 2010 24th IEEE international conference on, pages 400–407. IEEE.
Sakellariou, R., Zhao, H., Tsiakkouri, E., and Dikaiakos, M. D. (2007). Scheduling workflows with budget constraints. In Integrated research in GRID computing, pages 189–202. Springer.
Vaquero, L. M., Rodero-Merino, L., Caceres, J., and Lindner, M. (2008). A break in the clouds: towards a cloud definition. ACM SIGCOMM Computer Communication Review, 39(1):50–55.
Yang, M., Rutherfoord, B., and Qian, K. (2014). Learning cloud computing and security through cloudsim simulation. In Proceedings of the 2014 Information Security Curriculum Development Conference, InfoSec ’14, pages 6:1–6:1, New York, NY, USA. ACM.
Yu, J., Kirley, M., and Buyya, R. (2007). Multi-objective planning for workflow execution on grids. In Proceedings of the 8th IEEE/ACM International conference on Grid Computing, pages 10–17. IEEE Computer Society.
Zhao, Y., Hategan, M., Clifford, B., Foster, I., Von Laszewski, G., Nefedova, V., Raicu, I., Stef-Praun, T., and Wilde, M. (2007). Swift: Fast, reliable, loosely coupled parallel computation. In Services, 2007 IEEE Congress on, pages 199–206. IEEE.
Publicado
02/07/2017
Como Citar
RODRIGUES, Igor Barreto; DE OLIVEIRA, Daniel.
Uma Abordagem Hierárquica para Escalonamento de Workflows Científicos Executados em Nuvens. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 16. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1628-1641.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2017.3355.
