Data-Intensive Workflow Scheduling in Cloud on Budget and Deadline Constraints

  • Zhang Xin
  • Changze WuEmail author
  • Kaigui Wu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 201)


With the development of Cloud Computing, large-scale applications expressed as scientific workflows are often executed in cloud. The problems of workflow scheduling are vital for achieving high efficient and meeting the needs of users in clouds. In order to obtain more cost reduction as well as maintain the quality of service by meeting the deadlines, this paper proposed a novel heuristic, PWHEFT (Path-task Weight Heterogeneous Earliest Finish Time), based on Heterogeneous Earliest Finish Time (HEFT). The criticality of tasks in a workflow and data transmission between resources are considered in PWHEFT while ignored in some other algorithms. The heuristic is evaluated using simulation with five different real world workflow applications. The simulation results show that our proposed scheduling heuristic can significantly improve planning success rate.


Workflow HEFT Bi-criteria Data-intensive workflow scheduling 


  1. 1.
    Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)CrossRefGoogle Scholar
  2. 2.
    Juve, G., Deelman, E., Berriman, G.B., Berman, B.P., Maechling, P.: An evaluation of the cost and performance of scientific workflows on amazon ec2. J. Grid Comput. 10(1), 5–21 (2012)CrossRefGoogle Scholar
  3. 3.
    Prodan, R., Wieczorek, M.: Bi-criteria scheduling of scientific Grid workflows. IEEE Trans. Autom. Sci. Eng. 7, 364–376 (2010)CrossRefGoogle Scholar
  4. 4.
    Talukder, A.K.M., Kirley, M., Buyya, R.: Multiobjective differential evolution for scheduling workflow applications on global Grids. Concurr. Comput. Pract. Exp. 21(13), 1742–1756 (2009)CrossRefGoogle Scholar
  5. 5.
    Yu, J., Buyya, R.: Multi-objective planning for workflow execution on Grids. In: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 10–17 (2007)Google Scholar
  6. 6.
    Topcuoglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)CrossRefGoogle Scholar
  7. 7.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  8. 8.
    Wu, F., Wu, Q., Tan, Y.: Workflow scheduling in cloud: a survey. J. Supercomput., 71(9), 3373–3418Google Scholar
  9. 9.
    Kwok, Y.K., Ahmad, I.: Dynamic critical-path scheduling: an effective technique for allocating task graphs to multiprocessors. IEEE Trans. Parallel Distrib. Syst. 7(5), 506–521 (1996)CrossRefGoogle Scholar
  10. 10.
    Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architectures. IEEE Trans. Parallel Distrib. Syst. 4(2), 175–187 (1993)CrossRefGoogle Scholar
  11. 11.
    Verma, A., Kaushal, S.: Cost-Time efficient scheduling plan for executing workflows in the cloud. J. Grid Comput. 13(4), 1–12 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Rodrigo, N.C., Ranjan, R., Anton, B., Cesar, A.F.D.R., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. J. Softw. Pract. Exp. (SPE) 41(1), 23–50 (2011)CrossRefGoogle Scholar
  13. 13.
    Bharathi, S., Lanitchi, A., Deelman, E., Mehta, G., Su, M.H., Vahi, K.: Characterization of scientific workflows. In: Workshop on Workflows in Support of Large Scale Science, CA, USA, pp. 1–10 (2008)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.College of Computer ScienceChongqing UniversityChongqingChina

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