Abstract
A workflow is a set of steps or tasks that model the execution of a process, e.g., protein annotation, invoice generation and composition of astronomical images. Workflow applications commonly require large computational resources. Hence, distributed computing approaches (such as Grid and Cloud computing) emerge as a feasible solution to execute them. Two important factors for executing workflows in distributed computing platforms are (1) workflow scheduling and (2) resource allocation. As a consequence, there is a myriad of workflow scheduling algorithms that map workflow tasks to distributed resources subject to task dependencies, time and budget constraints. In this paper, we present a taxonomy of workflow scheduling algorithms, which categorizes the algorithms into (1) best-effort algorithms (including heuristics, metaheuristics, and approximation algorithms) and (2) quality-of-service algorithms (including budget-constrained, deadline-constrained and algorithms simultaneously constrained by deadline and budget). In addition, a workflow engine simulator was developed to quantitatively compare the performance of scheduling algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agrawal, K., Benoit, A., Magnan, L., Robert, Y.: Scheduling algorithms for linear workflow optimization. In: 2010 IEEE International Symposium on Parallel and Distributed Processing (IPDPS), pp. 1–12. IEEE (2010)
Bajaj, R., Agrawal, D.: Improving scheduling of tasks in a heterogeneous environment. IEEE Trans. Parallel Distrib. Syst. 15(2), 107–118 (2004)
Blythe, J., Jain, S., Deelman, E., Gil, Y., Vahi, K., Mandal, A., Kennedy, K.: Task scheduling strategies for workflow-based applications in grids. In: IEEE International Symposium on Cluster Computing and the Grid, CCGrid 2005, vol. 2, pp. 759–767. IEEE (2005)
Bmv, G.: Informe Anual 2012. Technical report, Bolsa Mexicana de Valores (2012)
Brandic, I., Pllana, S., Benkner, S.: Amadeus: a holistic service-oriented environment for grid workflows. In: Fifth International Conference on Grid and Cooperative Computing Workshops, GCCW 2006, pp. 259–266. IEEE (2006)
Kannas, C.C., Kalvari, I., Lambrinidis, G., Neophytou, M.C., Savva, G.C., Kirmitzoglou, I., Antoniou, Z., Achilleos, K.G., Scherf, D., Pitta, A.C., et al.: Lisis: an online scientific workflow system for virtual screening. Comb. Chem. High Throughput Screen. 18(3), 281–295 (2015)
Chekuri, C., Bender, M.: An efficient approximation algorithm for minimizing makespan on uniformly related machines. J. Algorithms 41(2), 212–224 (2001)
Deelman, E., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Patil, S., Su, M.H., Vahi, K., Livny, M.: Pegasus: mapping scientific workflows onto the grid. In: Grid Computing, pp. 11–20. Springer, Heidelberg (2004)
van Der Aalst, W.M., Ter Hofstede, A.H., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distrib. Parallel Databases 14(1), 5–51 (2003)
Dong, F., Akl, S.G.: Scheduling algorithms for grid computing: state of the art and open problems. Technical report (2006)
Kofler, K., Haq, I.U., Schikuta, E.: A parallel branch and bound algorithm for workflow QoS optimization. In: International Conference on Parallel Processing, ICPP 2009, pp. 478–485. IEEE (2009)
Li, S., Hu, S., Wang, S., Su, L., Abdelzaher, T., Gupta, I., Pace, R.: Woha: deadline-aware map-reduce workflow scheduling framework over hadoop clusters. In: 2014 IEEE 34th International Conference on Distributed Computing Systems (ICDCS), pp. 93–103. IEEE (2014)
Maheswaran, M., Ali, S., Siegal, H., Hensgen, D., Freund, R.F.: Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Proceedings of Eighth Heterogeneous Computing Workshop, (HCW 1999), pp. 30–44. IEEE (1999)
Mair, M., Qin, J., Wieczorek, M., Fahringer, T.: Workflow conversion and processing in the ASKALON grid environment. In: 2nd Austrian Grid Symposium, pp. 67–80. Citeseer (2007)
Menasce, D.A., Casalicchio, E.: A framework for resource allocation in grid computing. In: MASCOTS, pp. 259–267 (2004)
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer Science & Business Media, Berlin (2012)
Ramamritham, K., Stankovic, J.A., Shiah, P.F.: Efficient scheduling algorithms for real-time multiprocessor systems. IEEE Trans. Parallel Distrib. Syst. 1(2), 184–194 (1990)
Sakellariou, R., Zhao, H.: A hybrid heuristic for DAG scheduling on heterogeneous systems. In: Proceedings of 18th International Parallel and Distributed Processing Symposium, p. 111, April 2004
Sakellariou, R., Zhao, H., Tsiakkouri, E., Dikaiakos, M.D.: Scheduling workflows with budget constraints. In: Gorlatch, S., Danelutto, M. (eds.) Integrated Research in GRID Computing, pp. 189–202. Springer, Heidelberg (2007)
Shiers, J.: The worldwide LHC computing grid (worldwide LCG). Comput. Phys. Commun. 177(1), 219–223 (2007)
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)
Ullman, J.D.: NP-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)
Wieczorek, M., Hoheisel, A., Prodan, R.: Taxonomies of the multi-criteria grid workflow scheduling problem. In: Wieczorek, M., Hoheisel, A., Prodan, R. (eds.) Grid Middleware and Services, pp. 237–264. Springer, Heidelberg (2008)
Wieczorek, M., Hoheisel, A., Prodan, R.: Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener. Comput. Syst. 25(3), 237–256 (2009)
Young, L., McGough, S., Newhouse, S., Darlington, J.: Scheduling architecture and algorithms within the ICENI grid middleware. In: UK e-Science All Hands Meeting, pp. 5–12. Citeseer (2003)
Yu, J., Buyya, R.: A taxonomy of scientific workflow systems for grid computing. ACM Sigmod Rec. 34(3), 44–49 (2005)
Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci. Program. 14(3), 217–230 (2006)
Yu, J., Buyya, R., Ramamohanarao, K.: Workflow scheduling algorithms for grid computing. In: Xhafa, F., Abraham, A. (eds.) Metaheuristics for Scheduling in Distributed Computing Environments, pp. 173–214. Springer, Heidelberg (2008)
Yu, J., Buyya, R., Tham, C.K.: Cost-based scheduling of scientific workflow applications on utility grids. In: 2005 First International Conference on e-Science and Grid Computing, p. 8. IEEE (2005)
Acknowledgements
This work has been supported by Asociación Mexicana de Cultura A.C.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Aguilar-Reyes, F., Gutierrez-Garcia, J.O. (2017). A Taxonomy of Workflow Scheduling Algorithms. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-57972-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57971-9
Online ISBN: 978-3-319-57972-6
eBook Packages: Computer ScienceComputer Science (R0)