Duplication Based Performance Effective Scheduling

  • Monika SharmaEmail author
  • Raj Kumari
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 955)


In cloud computing (CC), mainly list scheduling algorithms are widely used in task scheduling. The existing list scheduling algorithms are generally not efficient in reducing overall execution time (makespan). So in this paper, we have presented a list scheduling algorithm namely, Performance Effective Task Scheduling (PETS) which is merged with the task duplication method named as Duplication based Performance Effective Scheduling (DPES). Most of the duplication algorithms mainly focus on obtaining high performance by minimizing the makespan without reviewing the energy consumed by an application. But DPES algorithm not only reduces the makespan but also examines the energy consumption. Duplication strategy is used in which the parent tasks have been replicated in order to minimize the makespan while to lower the energy consumption, Dynamic Voltage and Frequency Scaling (DVFS) technique has been applied. In this paper, the DPES algorithm is compared with the PETS algorithm on various performance metrics and DPES algorithm proves to be better in each metrics comparison.


Task scheduling List scheduling Task duplication DVFS 


  1. 1.
    Kim, W.: Cloud computing: today and tomorrow. J. Object Technol. 8(1), 65–72 (2009)CrossRefGoogle Scholar
  2. 2.
    Avram, M.G.: Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technol. 12, 529–534 (2014)CrossRefGoogle Scholar
  3. 3.
    Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., Ghalsasi, A.: Cloud computing-The business perspective. Decis. Support Syst. 51(1), 176–189 (2011)CrossRefGoogle Scholar
  4. 4.
    Chen, D., Zhao, H.: Data security and privacy protection issues in cloud computing. In: 2012 International Conference on Computer Science and Electronics Engineering, vol. 973, pp. 647–651 (2012)Google Scholar
  5. 5.
    Jensen, M., Schwenk, J., Gruschka, N., Lo Iacono, L.: On technical security issues in cloud computing. In: 2009 IEEE International Conference of Cloud Computing, pp. 109–116 (2009)Google Scholar
  6. 6.
    Selvi, S.T., Valliyammai, C., Dhatchayani, V.N.: Resource Allocation Issues and Challenges in Cloud Computing (2014)Google Scholar
  7. 7.
    Foster, I., Kesselman, C.: Globus: a metacomputing infrastructure toolkit. Int. J. High. Perform. Comput. Appl. 11(2), 115–128 (1997)Google Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    Topcuoglu, H., Hariri, S.: Task scheduling algorithms for heterogeneous processors. In: Proceedings of Eighth Heterogeneous Computing Workshop, pp. 3–14 (1999)Google Scholar
  10. 10.
    Nasr, A.A.: Task scheduling algorithm for high performance heterogeneous distributed computing systems. Int. J. Comput. Appl. 110(16), 23–29 (2015)Google Scholar
  11. 11.
    Lin, C., Lu, S.: Scheduling scientific workflows elastically for cloud computing. In: Proceeding-2011 IEEE 4th International Conference of Cloud Computing, pp. 746–747 (2011)Google Scholar
  12. 12.
    Nasr, A.A., El-Bahnasawy, N.A., El-Sayed, A.: Performance enhancement of scheduling algorithm in heterogeneous distributed computing systems. Int. J. Adv. Comput. Sci. Appl. 6(5), 88–96 (2015)Google Scholar
  13. 13.
    Zhenxia, Y., Fang, M., Sheng, S.: Scheduling algorithm based on task priority in heterogeneous computing environment. In: 2008 International Conference on Computer Science and Information Technology, vol. 19, no. 2, pp. 12–16 (2008)Google Scholar
  14. 14.
    Hosseinzadeh, M., Shahhoseini, H.S.: An effective duplication-based task-scheduling algorithm for heterogeneous systems. Simulation 87(12), 1067–1080 (2011)CrossRefGoogle Scholar
  15. 15.
    Bansal, S., Kumar, P., Singh, K.: An improved duplication strategy for scheduling precedence constrained graphs in multiprocessor systems. IEEE Trans. Parallel Distrib. Syst. 14(6), 533–544 (2003)CrossRefGoogle Scholar
  16. 16.
    Hagras, T., Janeček, J.: A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems. Parallel Comput. 31(7), 653–670 (2005)CrossRefGoogle Scholar
  17. 17.
    Mei, J., Li, K.: Energy-aware scheduling algorithm with duplication on heterogenous computing systems. In: Proceedings of the IEEE/ACM International Conference on Grid Computing, pp. 122–129 (2012)Google Scholar
  18. 18.
    Thambidurai, E.I.P., Mahilmannan, R.: Performance effective task scheduling algorithm for heterogeneous computing system. In: Proceedings of 4th International Symposium on Parallel Distributed Computing, pp. 0–7 (2005)Google Scholar
  19. 19.
    Huang, Q., Su, S., Li, J., Xu, P., Shuang, K., Huang, X.: Enhanced energy-efficient scheduling for parallel applications in cloud. In: 2012 12th IEEE/ACM International Symposium Cluster, Cloud and Grid Computing (CCGRID 2012), pp. 781–786 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.UIET, Panjab UniversityChandigarhIndia

Personalised recommendations