Skip to main content
SpringerLink
Log in

Granularity-based workflow scheduling algorithm for cloud computing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The workflow scheduling problem has drawn a lot of attention in the research community. This paper presents a workflow scheduling algorithm, called granularity score scheduling (GSS), which is based on the granularity of the tasks in a given workflow. The main objectives of GSS are to minimize the makespan and maximize the average virtual machine utilization. The algorithm consists of three phases, namely B-level calculation, score adjustment and task ranking and scheduling. We simulate the proposed algorithm using various benchmark scientific workflow applications, i.e., Cybershake, Epigenomic, Inspiral and Montage. The simulation results are compared with two well-known existing workflow scheduling algorithms, namely heterogeneous earliest finish time and performance effective task scheduling, which are also applied in cloud computing environment. Based on the simulation results, the proposed algorithm remarkably demonstrates its performance in terms of makespan and average virtual machine utilization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25:599–616

    Article  Google Scholar 

  2. Durao F, Carvalho JFS, Fonseka A, Garcia VC (2014) A systematic review on cloud computing. J Supercomput 68(3):1321–1346

    Article  Google Scholar 

  3. Lin W, Liang C, Wang JZ, Buyya R (2014) Bandwidth-aware divisible task scheduling for cloud computing. Softw Pract Exp 44:163–174

    Article  Google Scholar 

  4. Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58

    Article  Google Scholar 

  5. Ullman JD (1975) Np-complete scheduling problems. J Comput Syst Sci 10(3):384–393

    Article  MATH  MathSciNet  Google Scholar 

  6. Kwok YK, Ahmad I (1999) Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput Surv (CSUR) 31(4):406–471

    Article  Google Scholar 

  7. Braun TD, Siegel HJ, Beck N, Boloni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D, Freund RF (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810–837

    Article  MATH  Google Scholar 

  8. Yu J, Buyya R, Ramamohanarao K (2008) Workflow scheduling algorithms for grid computing. In: Proceedings of Metaheuristics for Scheduling in Distributed Computing Environments, Springer, New York, pp 173–214

  9. Xhafa F, Carretero J, Barolli L, Durresi A (2007) Immediate mode scheduling in grid systems. Int J Web Grid Serv 3(2):219–236

    Article  Google Scholar 

  10. Xhafa F, Barolli L, Durresi A (2007) Batch mode scheduling in grid systems. Int J Web Grid Serv 3(1):19–37

    Article  Google Scholar 

  11. Li J, Qiu M, Ming Z, Quan G, Qin X, Gu Z (2012) Online optimization for scheduling preemptable tasks on IaaS cloud system. J Parallel Distrib Comput 72:666–677

    Article  Google Scholar 

  12. Panda SK, Jana PK (2017) SLA-based task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 73(6):2730–2762

    Article  Google Scholar 

  13. Wen H, Hai-ying Z, Chuang L, Yang Y (2011) Effective load balancing for cloud-based multimedia system. In: International Conference on Electronic and Mechanical Engineering and Information Technology, pp 165–168

  14. Wang S, Yan K, Liao W, Wang S (2010) Towards a load balancing in a three-level cloud computing network. In: 3rd IEEE International Conference on Computer Science and Information Technology, vol 1, pp 108–113

  15. Panda SK, Gupta I, Jana PK (2017) Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Information system frontiers. Springer, Berlin

    Google Scholar 

  16. Bittencourt LF, Madeira ERM, Fonseca NLSD (2012) Scheduling in hybrid clouds. IEEE Commun Mag 50(9):42–47

    Article  Google Scholar 

  17. Kumar MS, Gupta I, Jana PK (2016) Forward load aware scheduling for data-intensive workflow applications in cloud system. In: 15th International Conference on Information Technology, Accepted (2016)

  18. Deldari A, Naghibzadeh M, Abrishami S (2017) CCA: a deadline-constrained workflow scheduling algorithm for multicore resources on the cloud. J Supercomput 73(2):756–781

    Article  Google Scholar 

  19. Bochenina K, Butakov N, Boukhanovsky A (2016) Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments. Future Gener Comput Syst 55:51–61

    Article  Google Scholar 

  20. Malawski M, Juve G, Deelman E, Nabrzyski J (2015) Algorithms for cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds. Future Gener Comput Syst 48:1–18

    Article  Google Scholar 

  21. Michael RG, Johnson DS (1979) Computers and intractability, a guide to the theory of np-completeness. WH Freeman Co., San Francisco

    MATH  Google Scholar 

  22. Smanchat S, Viriyapant K (2015) Taxonomies of workflow scheduling problem and techniques in the cloud. Future Gener Comput Syst 52:1–12

    Article  Google Scholar 

  23. Wieczorek M, Prodan R, Fahringer T (2005) Scheduling of scientific workflows in the ASKALON grid environment. ACM SIGMOD Rec 34(3):56–62

    Article  Google Scholar 

  24. Vasile M, Pop F, Tutueanu R, Cristea V, Kolodziej J (2015) Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener Comput Syst 51:61–71

    Article  Google Scholar 

  25. Rodriguez MA, Buyya R (2014) Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235

    Article  Google Scholar 

  26. El-Rewini H, Lewis TG (1990) Scheduling parallel program tasks onto arbitrary target machines. J Parallel Distrib Comput 9(2):138–153

    Article  MATH  Google Scholar 

  27. Gerasoulis A, Yang T (1993) On the granularity and clustering of directed acyclic task graphs. IEEE Trans Parallel Distrib Syst 4(6):686–701

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Ilavarasan E, Thambidurai P (2007) Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J Comput Sci 3(2):94–103

    Article  Google Scholar 

  30. Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694

    Article  Google Scholar 

  31. Ergu D, Kou G, Peng Y, Shi Y, Shi Y (2013) The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J Supercomput 64:835–848

    Article  Google Scholar 

  32. Ming G, Li H (2012) An improved algorithm based on max–min for cloud task scheduling, recent advances in computer science and information engineering, vol 125. lecture notes in electrical engineering. Springer, Berlin, pp 217–223

  33. Xu X, Hu H, Hu N, Ying W (2012) Cloud task and virtual machine allocation strategy in cloud computing environment, network computing and information security, communications in computer and information science. Springer, Berlin

    Google Scholar 

  34. OpenNebula. http://archives.opennebula.org

  35. Nimbus. http://www.nimbusproject.org

  36. Eucalyptus. http://manpages.ubuntu.com/manpages/precise/man5/eucalyptus.conf.5.htm

  37. Lee YC, Han H, Zomaya AY, Yousif M (2015) Resource-efficient workflow scheduling in clouds. Knowl Based Syst 80:153–162

    Article  Google Scholar 

  38. Su S, Li J, Huang Q, Huang X, Shuang K, Wang J (2013) Cost-efficient task scheduling for executing large programs in the cloud. Parallel Comput 39(4):177–188

    Article  Google Scholar 

  39. Masdari M, ValiKardan M, Shahi Z, Azar SI (2016) Towards workflow scheduling in cloud computing: a comprehensive analysis. J Netw Comput Appl 66:64–82

    Article  Google Scholar 

  40. Panda SK, Jana PK (2016) Normalization-based task scheduling algorithms for heterogeneous multi-cloud environment. Inform Syst Front. doi:10.1007/s10796-016-9683-5

  41. Gupta I, Kumar MS, Jana PK (2016) Compute-intensive workflow scheduling in multi-cloud environment. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp 315–321

  42. Hsu CH, Slagter KD, Chen SC, Chung YC (2014) Optimizing energy consumption with task consolidation in clouds. Inf Sci 258:452–462

    Article  Google Scholar 

  43. Panda SK, Jana PK (2016) Uncertainty-based QoS min–min algorithm for heterogeneous multi-cloud environment. Arab J Sci Eng 41(8):3003–3025

    Article  Google Scholar 

  44. Panda SK, Jana PK (2015) Efficient task scheduling algorithms for heterogeneous multi-cloud environment. J Supercomput 71(4):1505–1533

    Article  Google Scholar 

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

    Article  Google Scholar 

  46. Workflow Generator (2016) https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator. Accessed 2 June

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India

    Madhu Sudan Kumar, Indrajeet Gupta & Prasanta K. Jana

  2. Department of Computer Science and Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, 768018, India

    Sanjaya K. Panda

Authors
  1. Madhu Sudan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Indrajeet Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sanjaya K. Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Prasanta K. Jana
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madhu Sudan Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, M.S., Gupta, I., Panda, S.K. et al. Granularity-based workflow scheduling algorithm for cloud computing. J Supercomput 73, 5440–5464 (2017). https://doi.org/10.1007/s11227-017-2094-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-017-2094-7

Keywords

Search

Navigation