Skip to main content
SpringerLink
Log in

An efficient cost-based algorithm for scheduling workflow tasks in cloud computing systems

  • S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cloud computing has become a highly required platform in fields of information technology due to providing inexpensive services with high availability and scalability. The dynamic and diverse nature of the cloud computing systems makes scheduling of workflow tasks a pivotal issue. This paper proposes an algorithm to schedule applications’ tasks to virtual machines (VMs) of cloud computing systems. The algorithm has three phases: level sorting, task-prioritizing and virtual machine selection. The three-phase process successfully assigns the virtual machine for each task without making any difficulties for evaluating the algorithm performance; extensive simulation experiments are performed. The introduced ICTS algorithm analyzes each incoming task which is sorted and ranked while assigning the virtual machine to the particular task which improves the overall scheduling process because it processes the job according to the importance. Then the efficiency of the system is evaluated using experimental results that indicate the improved cost task scheduling (ICTS) algorithm provides an improvement in schedule length as well as significant monetary cost saving.

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

Similar content being viewed by others

References

  1. Kahanwal D, Singh TP (2012) The distributed computing paradigms: P2P, grid, cluster, cloud, and jungle. Int J Latest Res Sci Technol 1:183–187

    Google Scholar 

  2. Khurana S, Verma A (2013) Comparison of cloud computing service models: SaaS, PaaS, IaaS. Int J Electron Commun Technol 4(3):29–32

    Google Scholar 

  3. Furht B, Escalante A (2010) Handbook of cloud computing, 1st edn. Springer, Berlin

    Book  MATH  Google Scholar 

  4. Sireesha P, Deepthi R (2014) Analysis of cloud components and study on scheduling framework in local resource. Int J Sci Eng Technol Res (IJSETR) 3(10):2790–2794

    Google Scholar 

  5. Selvarani S, Udha G (2010) Improved cost-based algorithm for task scheduling in cloud computing. In: Proceedings of IEEE international conference on computational intelligence and computing research (ICCIC), Coimbatore, India, pp 1–5

  6. Mustafa S, Nazir B, Hayat A, Khan A, Madani S (2015) Resource management in cloud computing: taxonomy, prospects, and challenges. Comput Electr Eng 47:186–203

    Article  Google Scholar 

  7. Chawla Y, Bhonsle M (2012) A study on scheduling methods in cloud computing. Int J Emerg Trends Technol Comput Sci 1(3):12–17

    Google Scholar 

  8. 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 39:177–188

    Article  Google Scholar 

  9. Man N, Huh E (2013) Cost and efficiency-based scheduling on a general framework combining between cloud computing and local thick clients. In: Proceedings of international conference on computing, management and telecommunications (ComManTel), Ho Chi Minh City, Vietnam, pp 258–263

  10. Dubeya K, Kumarb M, Sharmaab S (2018) Modified HEFT algorithm for task scheduling in cloud environment. Procedia Comput Sci 125:725–732

    Article  Google Scholar 

  11. Li J, Su S, Cheng X, Huang Q, Zhang Z (2011) Cost-conscious scheduling for large graph processing in the cloud. In: Proceedings of IEEE international conference on high performance computing and communications, Banff, AB, Canda, pp 808–813

  12. Nasr A, El-Bahnasawy N, El-Sayed A (2014) Task scheduling optimization in heterogeneous distributed systems. Int J Comput Appl 107(4):5–7

    Google Scholar 

  13. Cao Q, Gong W, Wei Z (2009) An optimized algorithm for task scheduling based on activity based costing in cloud computing. In: Proceedings of third international conference on bioinformatics and biomedical engineering, Beijing, China, pp 1–3

  14. Guo-Ning G, Ting-Lei H (2010) genetic simulated annealing algorithm for task scheduling based on cloud computing environment. In: Proceedings of international conference on intelligent computing and integrated systems, Guilin, China, pp 60–63

  15. Geng X, Mao Y, Xiong M, Liu Y (2018) An improved task scheduling algorithm for scientific workflow in cloud computing environment. Springer, Berlin

    Book  Google Scholar 

  16. Alkhanaka E, Leea S, Rezaeia R, Parizi R (2016) Cost optimization approaches for scientific workflows scheduling in cloud and grid computing: a review, classifications, and open issues. J Syst Softw 113:1–26

    Article  Google Scholar 

  17. Zhou N, Qi D, Wang X, Zheng Z, Lin W (2016) A list scheduling algorithm for heterogeneous systems based on a critical node cost table and pessimistic cost table. Concurr Comput Pract Exp 29:1–11

    Google Scholar 

  18. Yang Y, Chen J, Liu X, Yuan D, Jin H (2008) An algorithm in SwinDeW-C for scheduling transaction intensive cost constrained cloud workflow. In: Proceedings of fourth IEEE international conference on eScience, pp 374–375

  19. Bahnasawy NA, Omara F, Qotb M (2011) A New algorithm for static task scheduling for heterogeneous distributed computing systems. Afr J Math Comput Sci Res 4(6):221–234

    Google Scholar 

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

    Article  Google Scholar 

  21. Isard M, Budiu M, Yu Y, Birrell A, Fetterly D (2007) Distributed data-parallel programs from sequential building blocks. ACM SIGOPS Oper Syst Rev 41(3):59–72

    Article  Google Scholar 

  22. Eswari R, Nickolas S (2010) Path-based heuristic task scheduling algorithm for heterogeneous distributed computing systems. In: Proceedings of international conference on advances in recent technologies in communication and computing, Kottayam, India, pp 30–34

  23. Sotiriadis S, Bessis N, Buyya R (2018) Self managed virtual machine scheduling in cloud systems. Inf Sci 433–434:381–400

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Rajavel R, Mala T (2010) Achieving service level agreement in cloud environment using job prioritization in hierarchical scheduling. In: Proceedings of international conference on information system design and intelligent application, vol. 132, pp 547–554

  26. Kumar S, Mittal S, Singh M (2017) A comparative study of metaheuristics based task scheduling in distributed environment. Indian J Sci Technol. https://doi.org/10.17485/ijst/2017/v10i26/97031

    Google Scholar 

  27. Babukarthik RG, Raju R, Dhavachelvan P (2012) Energy-aware scheduling using hybrid algorithm for cloud computing. In: Computing communication and networking technologies in IEEE

Download references

Acknowledgements

This work was supported by King Saud University, Deanship of Scientific Research, Community College Research Unit.

Author information

Authors and Affiliations

  1. Department of Computer Science, Community College, King Saud University, P. O. Box: 28095-11437, Riyadh, Saudi Arabia

    Mohammed Amoon

  2. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, Egypt

    Mohammed Amoon, Nirmeen El-Bahnasawy & Mai ElKazaz

Authors
  1. Mohammed Amoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nirmeen El-Bahnasawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mai ElKazaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammed Amoon.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amoon, M., El-Bahnasawy, N. & ElKazaz, M. An efficient cost-based algorithm for scheduling workflow tasks in cloud computing systems. Neural Comput & Applic 31, 1353–1363 (2019). https://doi.org/10.1007/s00521-018-3610-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-018-3610-2

Keywords

Search

Navigation