Parental Prioritization-Based Task Scheduling in Heterogeneous Systems

  • Muhammad Shahzad ArifEmail author
  • Zeshan Iqbal
  • Rehan Tariq
  • Farhan Aadil
  • Muhammad Awais
Research Article - Computer Engineering and Computer Science


Efficient task scheduling is important for achieving high performance in heterogeneous distributed computing systems. The main focus of this research is to build a task scheduling algorithm for a heterogeneous environment. We proposed an algorithm named parental prioritization earliest finish time. It has two phases, tasks prioritization phase and processor assigning phase. In the tasks prioritization phase, tasks will schedule in parental priority queue (PPQ) on the basis of downward rank and parental priority. Task prioritization is based on the directed acyclic graph. It can schedule the task of successor row before the current row if it has less communication cost. In the processor assigning phase, the processor will allocate to the scheduled tasks obtained from PPQ keeping the computation cost to a minimum. This proposed algorithm is compared with HEFT and CPOP algorithms through graphs generated from random task graph generator and a set of tasks. The experimental results show that our proposed scheduling algorithm performs significantly better than other algorithms in terms of both cost and makespan of schedules.


Heterogeneous systems Task scheduling Cloud computing Directed acyclic graph Parental priority queue 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Convolbo, M.W.; Chou, J.: Cost-aware DAG scheduling algorithms for minimizing execution cost on cloud resources. J. Supercomput. 72(3), 985–1012 (2016)CrossRefGoogle Scholar
  2. 2.
    Montage: An astronomical image engine. Accessed 19 Aug 2018
  3. 3.
    Epigenomics: USC Epigenome Center. Accessed 19 Aug 2018
  4. 4.
    Xie, Y.; Wu, J.; Liu, F.: A hierarchic hybrid scheduling algorithm for static task with precedence constraints. In: 2016 IEEE Trustcom/BigDataSE/ISPA, 2079-2085 (2016)Google Scholar
  5. 5.
    Zhang, L.; Li, K.; Xu, Y.; Mei, J.; Zhang, F.; Li, K.: Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster. Inf. Sci. 319, 113–131 (2015)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Zhang, Q.; Zhani, M.F.; Boutaba, R.; Hellerstein, J.L.: Dynamic heterogeneity-aware resource provisioning in the cloud. IEEE Trans. Cloud Comput. 2(1), 14–28 (2014)CrossRefGoogle Scholar
  7. 7.
    Wu, W.; Bouteiller, A.; Bosilca, G.; Faverge, M.; Dongarra, J.: Hierarchical DAG scheduling for hybrid distributed systems. In: 2015 IEEE International Conference on Parallel and Distributed Processing Symposium, pp. 156–165 (2015)Google Scholar
  8. 8.
    Keshanchi, B.; Souri, A.; Navimipour, N.J.: An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: formal verification, simulation, and statistical testing. J. Syst. Softw. 124, 1–21 (2017)CrossRefGoogle Scholar
  9. 9.
    Dai, Y.; Zhang, X.: A synthesized heuristic task scheduling algorithm. Sci. World J. 2014, 1–9 (2014)Google Scholar
  10. 10.
    Kumar, M.S.; Gupta, I.; Panda, S.K.; Jana, P.K.: Granularity-based workflow scheduling algorithm for cloud computing. J. Supercomput. 73(12), 5440–5464 (2017)CrossRefGoogle Scholar
  11. 11.
    Panda, S.K.; Jana, P.K.: Normalization-based task scheduling algorithms for heterogeneous multi-cloud environment. Inf. Syst. Front. 20(2), 373–399 (2018)CrossRefGoogle Scholar
  12. 12.
    Qasim, M.; Iqbal, T.; Munir, E. U.; Tziritas, N.; Khan, S. U.; Yang, L. T.: Dynamic mapping of application workflows in heterogeneous computing environments. In: IEEE international parallel and distributed processing symposium workshops, pp. 462–471 (2017)Google Scholar
  13. 13.
    Nasr, A.A.; Bahnasawy, N.A.E.; Sayed, A.E.: A new duplication task scheduling algorithm in heterogeneous distributed computing systems. Bull. Electr. Eng. Inform. 5(3), 373–382 (2016)Google Scholar
  14. 14.
    Eswari, R.; Nickolas, S.; Arock, M.: A path priority-based task scheduling algorithm for heterogeneous distributed systems. Int. J. Commun. Netw. Distrib. Syst. 12(2), 183–201 (2014)CrossRefGoogle Scholar
  15. 15.
    Panda, S.K.; Gupta, I.; Jana, P.K.: Task scheduling algorithms for multi-cloud systems: allocation-aware approach. Inf. Syst. Front. 19, 1–19 (2017)CrossRefGoogle Scholar
  16. 16.
    Jiang, Y.; Shao, Z.; Guo, Y.: A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization. Sci. World J. 2014, 1–23 (2014)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Engineering and TechnologyTaxilaPakistan
  2. 2.Department of Computer ScienceCOMSATS Institute of Science and TechnologyAttockPakistan

Personalised recommendations