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A budget constrained scheduling algorithm for executing workflow application in infrastructure as a service clouds

  • Robabeh Ghafouri
  • Ali Movaghar
  • Mehran Mohsenzadeh
Article
  • 34 Downloads

Abstract

Cloud computing technology, which is a new model of service provisioning in distributed systems, has been raised as a way to execute workflow applications. To profit from this technology for executing workflow applications, it is necessary to develop workflow scheduling algorithms that consider different QoS parameters such as execution time and cost. Therefore, in this paper, we focus on two criteria: makespan (completion time) and execution cost of workflow application and propose a scheduling algorithm named CB-DT (Constrained Budget-Decreased Time) which aims to create a schedule that decreases the makespan while satisfying the budget constraint of the workflow application. In the proposed algorithm, the ideas of back-tracking heuristic and scheduling of critical and non-critical tasks are combined together. In order to have smaller makespan, the proposed algorithm tries to select faster and more expensive machines for critical tasks as much as possible using the back-tracking method. Moreover, it tries to schedule non-critical tasks on the low cost machines as far as possible without increasing the makespan. The proposed algorithm is evaluated by a simulation process using WorkflowSim which is based on CloudSim. To evaluate the proposed algorithm, the results of proposed algorithm are compared with the results of IC-Loss (IaaS Cloud-Loss), BHEFT (Budget constrained Heterogeneous Earliest Finish Time) and BDHEFT(Budget and Deadline Constraint Heterogeneous Earliest Finish Time) algorithms. The results showed that the proposed algorithm performs better than IC-Loss, BHEFT and BDHEFT algorithms in most cases.

Keywords

Workflow application DAG Scheduling Budget constraint IaaS cloud 

References

  1. 1.
    Juve G, Deelman E, Vahi K, Mehta G, Berriman B, Berman BP, Maechling P (2010) Scientific workflow applications on amazon EC2. In: 5th IEEE international conference on e-scienceGoogle Scholar
  2. 2.
    Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K (2012) Characterizing and profiling scientific workflows. Futur Gener Comput Syst 29(3):682–692CrossRefGoogle Scholar
  3. 3.
    Mao M, Humphrey M (2011) Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: Proceedings of 2011 international conference for high performance computing, networking, storage and analysis, seattle, Washington , pp 1–49Google Scholar
  4. 4.
    Wu F, Wu Q, Tan Y (2015) Workflow scheduling in cloud: A survey. J Supercomput 71(9):3373–3418CrossRefGoogle Scholar
  5. 5.
    Hoffa C, Mehta G, Freeman T, Deelman E et al (2008) On the use of cloud computing for scientific workflows. In: Proceedings of the 2008 Fourth IEEE international conference on eScience, pp 640–645Google Scholar
  6. 6.
    Juve G, Deelman E (2011) Scientific workflows in the cloud. In: Grids, clouds and virtualization, Springer, pp 71–91Google Scholar
  7. 7.
    Abrishami S, Naghibzadeh M, Epema D (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Futur Gener Comput Syst 29(1):158–169CrossRefGoogle Scholar
  8. 8.
    Garey M, Johnson D (1990) Computers and intractability; A guide to the theory of NP-completeness. Freeman, San FranciscoMATHGoogle Scholar
  9. 9.
    Arabnejad H, Barbosa J, Prodan R (2016) Low-time complexity budget-deadline constrained workflow scheduling on heterogeneous resources. Futur Gener Comput Syst 55(c):29–40CrossRefGoogle Scholar
  10. 10.
    Calheiros R, Buyya R (2014) Meeting deadlines of scientific workflows in public clouds with tasks replication. IEEE Trans Parallel Distrib Syst 25(7):1787–1796CrossRefGoogle Scholar
  11. 11.
    Sahni J, Vidyarthi D (2015) A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment. IEEE Trans Cloud Comput 1(1):99Google Scholar
  12. 12.
    Chopra N, Singh S (2013) HEFT Based workflow scheduling algorithm for cost optimization within deadline in hybrid clouds. In: Proceeding of Fourth international conference on computing, communications and networking technologies (ICCCNT), India, pp 1–6Google Scholar
  13. 13.
    Yu J, Ramamohanarao K, Buyya R (2009) Deadline/Budget-Based Scheduling of workflows on utility grids. Market-oriented grid and utility computing. Wiley, New YorkGoogle Scholar
  14. 14.
    Yuan Y, Li X, Wang Q, Zhang Y (2008) Bottom level based heuristic for workflow scheduling in grids. Chin J Comput Chin 31(2):282CrossRefGoogle Scholar
  15. 15.
    Yuan Y, Li X, Wang Q, Zhu X (2009) Deadline division-based heuristic for cost optimization in workflow scheduling. Inform Sci 179(15):2562–2575CrossRefMATHGoogle Scholar
  16. 16.
    Arabnejad H, Barbosa J (2014) A budget constrained scheduling algorithm for workflow applications. J Grid Comput 12(4):665–679CrossRefGoogle Scholar
  17. 17.
    Chen W, Xie G, Li R, Bai R, Fan C, Li K (2017) Efficient task scheduling for budget constrained parallel applications on heterogeneous cloud. Futur Gener Comput Syst 74(C):1–11CrossRefGoogle Scholar
  18. 18.
    Sakellariou R, Zhao H et al (2007) Scheduling workflows with budget constraints. Integrated research in GRID computing. Springer, USA. ISBN 978-0-387-47658-2Google Scholar
  19. 19.
    Zeng L, Veeravalli B, Li X (2012) Budget conscious scheduling precedence-constrained many-task workflow applications in cloud. In: Proceedings of IEEE 26th international conference on advanced information networking and applications, FukuokaGoogle Scholar
  20. 20.
    Wu C, Lin X, Yu D, Xu W, Li L (2015) End-to-end delay minimization for scientific workflows in clouds under budget constraint. IEEE Trans Cloud Comput 3(2):169–181CrossRefGoogle Scholar
  21. 21.
    Su S, Li J, Huang Q, Wang J (2013) Cost-efficient task scheduling for executing large program in the cloud. J Parallel Comput 39:177–188CrossRefGoogle Scholar
  22. 22.
    Zheng W, Sakellariou R (2013) Budget-deadline constrained workflow planning for admission control. J Grid Comput 11(4):633–651CrossRefGoogle Scholar
  23. 23.
    Topcuouglu H, Hariri S, Wu M (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274CrossRefGoogle Scholar
  24. 24.
    Verma A, Kaushal S (2015) Cost-time efficient scheduling plan for executingworkflows in the cloud. J Grid Comput 13(4):495–506MathSciNetCrossRefGoogle Scholar
  25. 25.
    Malawski M, Juve G, Deelman E, Nabrzyski J (2015) Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in iaas clouds. Futur Gener Comput Syst 48(C):1–18CrossRefGoogle Scholar
  26. 26.
    Yu J, Buyya R (2006) Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14(3):217–230Google Scholar
  27. 27.
    Pandey S, Wu L, Guru SM, Buyya R (2010) A particle swarm optimizationbased heuristic for scheduling workflow applications in cloud computing environments. In: 24th IEEE international conference on Advanced information networking and applications (AINA), IEEE, pp 400–407Google Scholar
  28. 28.
    Liu L, Zhang M, Buyya R, Fan Q (2017) Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing. Concurrency Computat Pract Exper 29(5):e3942.  https://doi.org/10.1002/cpe.3942 CrossRefGoogle Scholar
  29. 29.
    Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694CrossRefGoogle Scholar
  30. 30.
    Canon L, Jeannot E, Sakellariou R, Zheng W (2008) Comparative evaluation of the robustness of DAG scheduling heuristics. In: Grid computing achievements and prospects. Springer, USA, pp 73–84Google Scholar
  31. 31.
    Alkhanak E, Lee S, Rezaei R (2016) Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. J Syst Softw 113(c):1–26CrossRefGoogle Scholar
  32. 32.
    Bryk P, Malawski M, Juve G, Deelman E (2016) Storage-aware algorithms for scheduling of workflow ensembles in clouds. J Grid Comput 14(2):359–378CrossRefGoogle Scholar
  33. 33.
    Zhang S, Chen X, Huo X (2010) Cloud computing research and development trend. In: Second international conference on Future networks, 2010. ICFN ’10, pp 93–97Google Scholar
  34. 34.
    Chen W, Deelman E (2012) WorkflowSim: A toolkit for simulating scientific workflows in distributed environments. In: 2012 IEEE 8th international conference on E-science (e-science), pp 1–8Google Scholar
  35. 35.
    Calheiros R, Ranjan R, Beloglazov A, De R, Buyya R (2011) Cloudsim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exper 14(1):23–50CrossRefGoogle Scholar
  36. 36.
    Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows. In: 2008 Third workshop on workflows in support of large-scale science, pp 1–10Google Scholar
  37. 37.
    Ostermann S, Iosup A, Yigitbasi N, Prodan R, Fahringer T, Epema D (2010) A performance analysis of EC2 cloud computing services for scientific computing. In: Cloud computing, Berlin, pp 115–131Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Computer EngineeringSharif University of TechnologyTehranIran

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