A Weighted Bi-objective Strategy for Executing Scientific Workflows in Containerized Environments
Resumo
Scientific workflows support the execution of complex simulation-based experiments across heterogeneous computing environments. Containerization technologies, such as Docker, improve portability by encapsulating tasks together with their dependencies. However, they also introduce challenges in resource management, as containers incur additional memory and CPU overhead and may execute concurrently on the same virtual or physical machine. These challenges are particularly critical in memory-constrained environments, where inefficient scheduling can lead to performance degradation or even task failures. To address this issue, we propose a weighted bi-objective scheduling strategy that considers memory consumption and execution time, allowing users to prioritize one objective or achieve a balance between the two. Experimental evaluations with both synthetic and real-world workflows demonstrate that our approach enhances performance and resource utilization.Referências
Alves, M. M. and Drummond, L. (2017). A multivariate and quantitative model for predicting cross-application interference in virtual environments. J. of Systems and Soft., 128:150–163.
Babuji, Y. N., Woodard, A., et al. (2019). Parsl: Pervasive parallel programming in python. In HPDC 2019, pages 25–36. ACM.
De Lima, M., Teylo, L., et al. (2024). An analysis of performance variability in aws virtual machines. In SSCAD 2024, pages 312–323. SBC.
de Oliveira, D. et al. (2012). A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds. J. Grid Comput., 10(3):521–552.
de Oliveira, D. C. M., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Synthesis Lectures on Data Management. Morgan & Claypool Publishers.
Deelman, E., da Silva, R. F., et al. (2021). The pegasus workflow management system: Translational computer science in practice. J. Comput. Sci., 52:101200.
Di Tommaso, P., Chatzou, M., et al. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4):316–319.
Ferreira, W. et al. (2024). Akôflow: um middleware para execução de workflows científicos em múltiplos ambientes conteinerizados. In SBBD 2024, pages 27–39, Florianópolis/SC. SBC.
Karmakar, K., Tarafdar, A., Das, R. K., and Khatua, S. (2024). Cost-efficient workflow as a service using containers. Journal of Grid Computing, 22(1):40.
Li, W., Li, X., and Ruiz, R. (2021). Scheduling microservice-based workflows to containers in on-demand cloud resources. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 61–66.
Muntz, R. and Coffman, E. (1969). Optimal preemptive scheduling on two-processor systems. IEEE Transactions on Computers, C-18(11):1014–1020.
Ogasawara, E. S., de Oliveira, D., et al. (2011). An algebraic approach for data-centric scientific workflows. Proc. VLDB Endow., 4(12):1328–1339.
Rajasekar, P. and Palanichamy, Y. (2021). Scheduling multiple scientific workflows using containers on iaas cloud. Journal of Ambient Intelligence and Humanized Computing, 12(7):7621–7636.
Sakellariou, R., Zhao, H., and Deelman, E. (2009). Mapping workflows on grid resources: Experiments with the montage workflow. In Proc. of the CoreGRID ERCIM Working Group Workshop, pages 119–132. Springer.
Shan, C., Xia, Y., Zhan, Y., and Zhang, J. (2023). Kubeadaptor: A docking framework for workflow containerization on kubernetes. FGCS, 148:584–599.
Struhár, V., Behnam, M., Ashjaei, M., and Papadopoulos, A. V. (2020). Real-time containers: A survey. In Fog-IoT, volume 80 of OASIcs, pages 7:1–7:9.
Sun, Z., Huang, H., Li, Z., and Gu, C. (2025). Energy-efficient real-time multi-workflow scheduling in container-based cloud. Journal of Combinatorial Optimization, 49(2):34.
Suter, F., Coleman, T., et al. (2026). A terminology for scientific workflow systems. FGCS, 174:107974.
Teylo, L., de Paula Junior, U., Frota, Y., de Oliveira, D., and Drummond, L. M. A. (2017). A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. FGCS, 76:1–17.
Zheng, C. and Thain, D. (2015). Integrating containers into workflows: A case study using makeflow, work queue, and docker. VTDC ’15, page 31–38, New York, NY, USA.
Zheng, C., Tovar, B., and Thain, D. (2017). Deploying high throughput scientific workflows on container schedulers with makeflow and mesos. In CCGRID, pages 130–139.
Babuji, Y. N., Woodard, A., et al. (2019). Parsl: Pervasive parallel programming in python. In HPDC 2019, pages 25–36. ACM.
De Lima, M., Teylo, L., et al. (2024). An analysis of performance variability in aws virtual machines. In SSCAD 2024, pages 312–323. SBC.
de Oliveira, D. et al. (2012). A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds. J. Grid Comput., 10(3):521–552.
de Oliveira, D. C. M., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Synthesis Lectures on Data Management. Morgan & Claypool Publishers.
Deelman, E., da Silva, R. F., et al. (2021). The pegasus workflow management system: Translational computer science in practice. J. Comput. Sci., 52:101200.
Di Tommaso, P., Chatzou, M., et al. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4):316–319.
Ferreira, W. et al. (2024). Akôflow: um middleware para execução de workflows científicos em múltiplos ambientes conteinerizados. In SBBD 2024, pages 27–39, Florianópolis/SC. SBC.
Karmakar, K., Tarafdar, A., Das, R. K., and Khatua, S. (2024). Cost-efficient workflow as a service using containers. Journal of Grid Computing, 22(1):40.
Li, W., Li, X., and Ruiz, R. (2021). Scheduling microservice-based workflows to containers in on-demand cloud resources. In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 61–66.
Muntz, R. and Coffman, E. (1969). Optimal preemptive scheduling on two-processor systems. IEEE Transactions on Computers, C-18(11):1014–1020.
Ogasawara, E. S., de Oliveira, D., et al. (2011). An algebraic approach for data-centric scientific workflows. Proc. VLDB Endow., 4(12):1328–1339.
Rajasekar, P. and Palanichamy, Y. (2021). Scheduling multiple scientific workflows using containers on iaas cloud. Journal of Ambient Intelligence and Humanized Computing, 12(7):7621–7636.
Sakellariou, R., Zhao, H., and Deelman, E. (2009). Mapping workflows on grid resources: Experiments with the montage workflow. In Proc. of the CoreGRID ERCIM Working Group Workshop, pages 119–132. Springer.
Shan, C., Xia, Y., Zhan, Y., and Zhang, J. (2023). Kubeadaptor: A docking framework for workflow containerization on kubernetes. FGCS, 148:584–599.
Struhár, V., Behnam, M., Ashjaei, M., and Papadopoulos, A. V. (2020). Real-time containers: A survey. In Fog-IoT, volume 80 of OASIcs, pages 7:1–7:9.
Sun, Z., Huang, H., Li, Z., and Gu, C. (2025). Energy-efficient real-time multi-workflow scheduling in container-based cloud. Journal of Combinatorial Optimization, 49(2):34.
Suter, F., Coleman, T., et al. (2026). A terminology for scientific workflow systems. FGCS, 174:107974.
Teylo, L., de Paula Junior, U., Frota, Y., de Oliveira, D., and Drummond, L. M. A. (2017). A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. FGCS, 76:1–17.
Zheng, C. and Thain, D. (2015). Integrating containers into workflows: A case study using makeflow, work queue, and docker. VTDC ’15, page 31–38, New York, NY, USA.
Zheng, C., Tovar, B., and Thain, D. (2017). Deploying high throughput scientific workflows on container schedulers with makeflow and mesos. In CCGRID, pages 130–139.
Publicado
28/10/2025
Como Citar
FERREIRA, Wesley; KUNSTMANN, Liliane; FROTA, Yuri; TEYLO, Luan; OLIVEIRA, Daniel de.
A Weighted Bi-objective Strategy for Executing Scientific Workflows in Containerized Environments. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 26. , 2025, Bonito/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 350-361.
DOI: https://doi.org/10.5753/sscad.2025.16730.
