[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing is an emerging paradigm that offers various services for both users and enterprisers. Scheduling of user tasks among data centers, host and virtual machines (VMs) becomes challenging issues in cloud due to involvement of vast number of users. To address such issues, a new multi-criteria approach i.e., technique of order precedence by similarity to ideal solution (TOPSIS) algorithm is introduced to perform task scheduling in cloud systems. The task scheduling is performed in two phases. In first phase, TOPSIS algorithm is applied to obtain the relative closeness of tasks with respect to selected scheduling criteria (i.e., execution time, transmission time and cost). In second phase the particle swarm optimization (PSO) begins with computing relative closeness of the given three criteria for all tasks in all VMs. A weighted sum of execution time, transmission time and cost used as an objective function by TOPSIS to solve the problem of multi-objective task scheduling in cloud environment. The simulation work has been done in CloudSim. The performance of proposed work has been compared with PSO, dynamic PSO (DPSO), ABC, IABC and FUGE algorithms on the basis of MakeSpan, transmission time, cost and resource utilization. Experimental results show approximate 75% improvement on average utilization of resources than PSO. Processing cost of TOPSIS–PSO reduced at approximate 23.93% and 55.49% than IABC and ABC respectively. The analysis also shows that TOPSIS–PSO algorithm reduces 3.1, 29.1 and 14.4% MakeSpan than FUGE, ant colony optimization (ACO) and multiple ACO respectively. Plotted graphs and calculated values show that the proposed work is very innovative and effective for task scheduling. This TOPSIS method to calculate relative closeness for PSO has been remarkable.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Li, Q., Hao, Q., Xiao, L., Li, Z.: Adaptive management of virtualized resources in cloud computing using feedback control. In: First International Conference on Information Science and Engineering, Nanjing, China, pp. 99–102. IEEE (2009)

  2. Parikh, K., Hawanna, N., Haleema, P.K., Jayasubalakshmi, R., Iyengar, N.: Virtual machine allocation policy in cloud computing using CloudSim in Java. Int. J. Grid Distrib. Comput. 8(1), 145–158 (2015)

    Article  Google Scholar 

  3. Tawfeek, M., El-Sisi, A., Keshk, A., Torkey, F.: Cloud task scheduling based on ant colony optimization. Int. Arab J. Inf. Technol. 12(2), 129–137 (2015)

    Google Scholar 

  4. Zhan, Z., Liu, X., Gong, Y., Zhang, J.: Cloud computing resource scheduling and a survey of its evolutionary approaches. ACM Comput. Surv. (2015). https://doi.org/10.1145/2788397

    Article  Google Scholar 

  5. Panwar, N., Rauthan, M.S.: Analysis of various task scheduling algorithms in cloud environment: review. In: 7th International Conference on Cloud Computing, Data Science and Engineering—Confluence, pp. 255–261. IEEE (2017)

  6. Pooranian, Z., Shojafar, M., Abawajy, J.H., Abraham, A.: An efficient meta-heuristic algorithm for grid computing. J. Comb. Optim. 30(3), 413–434 (2015)

    Article  MathSciNet  Google Scholar 

  7. Shojafar, M., Javanmardi, S., Abolfazli, Saeid., Cordeschi, Nicola.: FUGE: a joint meta-heuristic approach to cloud job scheduling algorithm using fuzzy theory and a genetic method. Clust. Comput. 18(2), 829–844 (2015)

    Article  Google Scholar 

  8. Tsou, C.: Multi-objective inventory planning using MOPSO and TOPSIS. Expert Syst. Appl. 35, 136–142 (2008)

    Article  Google Scholar 

  9. Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., Ignatius, J.: A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39(17), 13051–13069 (2012)

    Article  Google Scholar 

  10. Nelson Jayakumar, D., Venkatesh, P.: Glowworm swarm optimization algorithm with TOPSIS for solving multiple objective environmental economic dispatch problem. Appl. Soft Comput. 23, 375–386 (2014)

    Article  Google Scholar 

  11. Jia, L., Zou, G., Fan, L.: Combining TOPSIS and particle swarm optimization for a class of nonlinear bilevel programming problems. In: 10th International Conference on Computational Intelligence and Security. IEEE (2014). https://doi.org/10.1109/cis.2014.52

  12. Wang, Y., Li, B., Weise, T., Wang, J., Yuan, B., Tian, Q.: Self-adaptive learning based particle swarm optimization. Inf. Sci. 181(20), 4515–4538 (2011)

    Article  MathSciNet  Google Scholar 

  13. Zhang, P., Zhou, M.: Dynamic cloud task scheduling based on a two-stage strategy. IEEE Trans. Autom. Sci. Eng. 99, 1–12 (2017)

    Google Scholar 

  14. Al-maamari, A., Omara, F.A.: Task scheduling using PSO algorithm in cloud computing environments. Int. J. Grid Distrib. Comput. 8(5), 245–256 (2015)

    Article  Google Scholar 

  15. Zhan, S., Huo, H.: Improved PSO-based task scheduling algorithm in cloud computing. J. Inf. Comput. Sci. 9(13), 3821–3829 (2012)

    Google Scholar 

  16. Panwar, N., Negi, S., Rauthan, M.S.: Non-live task migration approach for scheduling in cloud based applications. In: NGCT 2017, CCIS, vol. 828, pp. 124–137 (2018)

    Google Scholar 

  17. Awad, A.I., El-Hefnawy, N.A., Abdel_kader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)

    Article  Google Scholar 

  18. Lakraa, A.V., Yadav, D.K.: Multi-objective tasks scheduling algorithm for cloud computing throughput optimization. Procedia Comput. Sci. 48, 107–113 (2015)

    Article  Google Scholar 

  19. Cho, K.M., Tsai, P.W., Tsai, C.W., Yang, C.S.: A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)

    Article  Google Scholar 

  20. Jena, R.K.: Multi objective task scheduling in cloud environment using nested PSO framework. Procedia Comput. Sci. 57, 1219–1227 (2015)

    Article  Google Scholar 

  21. Ghanbari, S., Othman, M.: A priority based job scheduling algorithm in cloud computing. In: International Conference on Advances Science and Contemporary Engineering (ICASCE 2012), vol. 50, pp. 778–785 (2012)

    Article  Google Scholar 

  22. Lawrance, H., Silas, S.: Efficient QoS based resource scheduling using PAPRIKA method for cloud computing. Int. J. Eng. Sci. Technol. 5(03), 638–643 (2013)

    Google Scholar 

  23. Shih, H.S., Shyur, H.J., Lee, E.S.: An extension of TOPSIS for group decision making. Math. Comput. Model. 45, 801–813 (2007)

    Article  Google Scholar 

  24. Selvarani, S., Sadhasivam, G.S.: Improved cost-based algorithm for task scheduling in cloud computing. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–5 (2010). https://doi.org/10.1109/ICCIC.2010.5705847

  25. Lin, W., Liang, C., Wang, J.Z., Buyya, R.: Bandwidth-aware divisible task scheduling for cloud computing. Softw. Pract. Exp. 44, 163–174 (2012)

    Article  Google Scholar 

  26. Zhang, Q., Liang, H., Xing, Y.: A parallel task scheduling algorithm based on fuzzy clustering in cloud computing environment. Int. J. Mach. Learn. Comput. 4(5), 437–444 (2014)

    Article  Google Scholar 

  27. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exp. 41, 23–50 (2011)

    Article  Google Scholar 

  28. Ruhela, D.S.: A study of computational complexity of algorithms for numerical methods. PhD Thesis, University of Rajasthan, Rajasthan, India (2014)

  29. Hamdani, H., Wardoyo, R.: The complexity calculation for group decision making using TOPSIS algorithm. AIP Conf. Proc. (2016). https://doi.org/10.1063/1.4958502

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering, HNBGU, Srinagar, Pauri, Uttarakhand, India

    Neelam Panwar & Man Mohan Singh Rauthan

  2. Computer Science and Engineering, UTU, Dehra Dun, Uttarakhand, India

    Sarita Negi

  3. Computer Science and Engineering, KEC, Dwarhat, Almora, Uttarakhand, India

    Kunwar Singh Vaisla

Authors
  1. Neelam Panwar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarita Negi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Man Mohan Singh Rauthan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kunwar Singh Vaisla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neelam Panwar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panwar, N., Negi, S., Rauthan, M.M.S. et al. TOPSIS–PSO inspired non-preemptive tasks scheduling algorithm in cloud environment. Cluster Comput 22, 1379–1396 (2019). https://doi.org/10.1007/s10586-019-02915-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-019-02915-3

Keywords

Search

Navigation