Abstract
Distributed scientific applications are commonly executed as a workflow of data interdependent tasks on a cluster of different machines. Over the last years, the infrastructure used for solving these problems has evolved from clusters of physical machines to virtual resources in a Cloud based on Quality of Service requirements and pay-per-use basis. In these settings, the total execution time of the workflow, i.e., the makespan, is one of the main objectives. The subsequent optimization problem of distributing the tasks on the available resources, called workflow scheduling problem, is often solved by means of metaheuristics. In this paper we propose an improved workflow model that considers disk times in communications costs. To solve the scheduling problem, we devise a genetic algorithm that produces robust schedules. The experimental study showed that the proposed model is able to predict the execution time of the workflow with more precision than the existing ones in a Cloud Infrastructure as a Services system.
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References
Casanova, H., et al.: Developing accurate and scalable simulators of production workflow management systems with WRENCH. Future Gener. Comput. Syst. 112, 162–175 (2020)
Chakravarthi, K.K., Neelakantan, P., Shyamala, L., Vaidehi, V.: Reliable budget aware workflow scheduling strategy on multi-cloud environment. Cluster Comput. (2022). https://doi.org/10.1007/S10586-021-03464-4
Coleman, T., Casanova, H., Pottier, L., Kaushik, M., Deelman, E., Ferreira da Silva, R.: WfCommons: a framework for enabling scientific workflow research and development. Future Gener. Comput. Syst. 128, 16–27 (2022)
Ghorbannia Delavar, A., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Cluster Comput. 17(1), 129–137 (2014)
Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Future Gener. Comput. Syst. 29(3), 682–692 (2013)
Madni, S.H.H., Abd Latiff, M.S., Abdullahi, M., Abdulhamid, S.M., Usman, M.J.: Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PLoS ONE 12(5), 1–26 (2017)
Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)
Ye, X., Li, J., Liu, S., Liang, J., Jin, Y.: A hybrid instance-intensive workflow scheduling method in private cloud environment. Natural Comput. 18(4), 735–746 (2017). https://doi.org/10.1007/s11047-016-9600-3
Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)
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This research has been supported by the Spanish Government under research grant PID2019-106263RB-I00.
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Barredo, P., Puente, J. (2022). Robust Makespan Optimization via Genetic Algorithms on the Scientific Workflow Scheduling Problem. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. IWINAC 2022. Lecture Notes in Computer Science, vol 13259. Springer, Cham. https://doi.org/10.1007/978-3-031-06527-9_8
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DOI: https://doi.org/10.1007/978-3-031-06527-9_8
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