Assessing the Impact of Supporting Information on the Scheduling of Scientific Workflows on Clouds

• Eduardo Cotrin Teixeira UTFPR
• Daniel Cordeiro USP
• Kelly Rosa Braghetto USP

DOI: https://doi.org/10.5753/bresci.2018.3277

Abstract

Executing scientific workflows in high-performance cloud computing platforms requires the use of scheduling algorithms that allow workflows execution as fast as possible, while minimizing the monetary cost of such executions. In this work we study how the use of supporting information can offer guidance to scheduling algorithms, helping them to devise more efficient execution plans in terms of the total execution time (makespan) and the total monetary cost. Using two large-scale scientific workflows, our experiments showed that simple modifications on a classical scheduling algorithm (HEFT), in conjunction with the appropriate supporting information, could reduce the monetary cost of an execution in up to 59% and reduce the makespan in up to 8.6%.

References

Cuevas-Vicenttin, V. et al. (2012). Scientific workflows and provenance: Introduction and research opportunities. Datenbank-Spektrum, 12(3):193–203.

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.

Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., and Vahi, K. (2013). Characterizing and profiling scientific workflows. Future Generation Computer Systems, 29(3):682–692.

Topcuouglu, H., Hariri, S., and Wu, M.-Y. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst., 13(3):260–274.