A Hybrid Meta-heuristic Approach for Load Balanced Workflow Scheduling in IaaS Cloud

  • Indrajeet GuptaEmail author
  • Shivangi Gupta
  • Anubhav Choudhary
  • Prasanta K. Jana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11319)


Workflow scheduling is one of the most-focused research problems in the field of cloud computing. This is a well known NP-complete problem and therefore finding an optimal solution in respect of various parameters such as makespan, resource utilization, energy, QoS or their combination is computationally very expensive. Nevertheless, load balancing among the virtual machines (VMs) is one of the most important aspects while scheduling tasks of the workflow. In this paper, we propose a hybrid meta-heuristic approach for workflow scheduling for IaaS cloud which is shown to be load balanced. The proposed algorithm is based on hybridization of genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm takes advantages of both the algorithms by avoiding slower convergence rate of GA and local optimum problem in PSO. The objective of the proposed algorithm is to map the tasks of the workflow to the VMs, such that the overall workflow execution time (makespan) is minimized and the assigned load on each VM is also balanced. With the rigorous experiments on scientific workflows, we show that the proposed approach performs better than PSO, GA and MPQGA (multiple priority queues genetic algorithm) based workflow scheduling algorithms. We also validate the better performance through a statistical test, i.e., paired t test with 95% confidence interval.


Workflow scheduling Cloud computing Meta-heuristic Load-balancing Makespan 


  1. 1.
    Ahmad, S.G., Liew, C.S., Munir, E.U., Ang, T.F., Khan, S.U.: A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J. Parallel Distrib. Comput. 87, 80–90 (2016)CrossRefGoogle Scholar
  2. 2.
    Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)CrossRefGoogle Scholar
  3. 3.
    Alkhanak, E.N., Lee, S.P., Rezaei, R., Parizi, R.M.: Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: a review, classifications, and open issues. J. Syst. Softw. 113, 1–26 (2016)CrossRefGoogle Scholar
  4. 4.
    Man, K.-F., Tang, K.-S., Kwong, S.: Genetic algorithms: concepts and applications in engineering design. IEEE Trans. Ind. Electron. 43(5), 519–534 (1996)CrossRefGoogle Scholar
  5. 5.
    Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: 1995 Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS 1995, pp. 39–43. IEEE (1995)Google Scholar
  6. 6.
    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
  7. 7.
    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)Google Scholar
  8. 8.
    Li, R., Huang, W., Yuan, Q.: Grid task scheduling using mutation particle swarm algorithm. In: IEEE Conference Anthology, pp. 1–3. IEEE (2013)Google Scholar
  9. 9.
    Juve, G., Chervenak, A., Deelman, E., Bharathi, S., Mehta, G., Vahi, K.: Characterizing and profiling scientific workflows. Futur. Gener. Comput. Syst. 29(3), 682–692 (2013)CrossRefGoogle Scholar
  10. 10.
    Zhu, Z., Zhang, G., Li, M., Liu, X.: Evolutionary multi-objective workflow scheduling in cloud. IEEE Trans. Parallel Distrib. Syst. 27(5), 1344–1357 (2016)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Tao, F., Feng, Y., Zhang, L., Liao, T.W.: CLPS-GA: a case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Appl. Soft Comput. 19, 264–279 (2014)CrossRefGoogle Scholar
  13. 13.
    Zhong, H., Tao, K., Zhang, X.: An approach to optimized resource scheduling algorithm for open-source cloud systems. In: 2010 Fifth Annual ChinaGrid Conference (ChinaGrid), pp. 124–129. IEEE (2010)Google Scholar
  14. 14.
    Delavar, A.G., Aryan, Y.: HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems. Clust. Comput. 17(1), 129–137 (2014)CrossRefGoogle Scholar
  15. 15.
    Choudhary, A., Gupta, I., Singh, V., Jana, P.K.: A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing. Futur. Gener. Comput. Syst. 83, 14–26 (2018)CrossRefGoogle Scholar
  16. 16.
    He, X.S., Sun, X.H., Von Laszewski, G.: QoS guided min-min heuristic for grid task scheduling. J. Comput. Sci. Technol. 18(4), 442–451 (2003)CrossRefGoogle Scholar
  17. 17.
    Mao, Y., Chen, X., Li, X.: Max–min task scheduling algorithm for load balance in cloud computing. In: Patnaik, S., Li, X. (eds.) CSAIT 2013. AISC, vol. 255, pp. 457–465. Springer, New Delhi (2014). Scholar
  18. 18.
    Zhan, Z.-H., Zhang, G.-Y., Gong, Y.-J., Zhang, J., et al.: Load balance aware genetic algorithm for task scheduling in cloud computing. In: Dick, G., et al. (eds.) SEAL 2014. LNCS, vol. 8886, pp. 644–655. Springer, Cham (2014). Scholar
  19. 19.
    Omara, F.A., Arafa, M.M.: Genetic algorithms for task scheduling problem. J. Parallel Distrib. Comput. 70(1), 13–22 (2010)CrossRefGoogle Scholar
  20. 20.
    Wang, X., Yeo, C.S., Buyya, R., Su, J.: Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm. Futur. Gener. Comput. Syst. 27(8), 1124–1134 (2011)CrossRefGoogle Scholar
  21. 21.
    Rodriguez, M.A., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    Xu, Y., Li, K., Hu, J., Li, K.: A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf. Sci. 270, 255–287 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Indrajeet Gupta
    • 1
    Email author
  • Shivangi Gupta
    • 1
  • Anubhav Choudhary
    • 1
  • Prasanta K. Jana
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (ISM), DhanbadDhanbadIndia

Personalised recommendations