Hybrid PPSO Algorithm for Scheduling Complex Applications in IoT

  • Komal MiddhaEmail author
  • Amandeep Verma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 924)


The Internet of Things (IoT) is boosting revolution in almost every aspect of our lives. It provides networking to connect things, applications, people, data with the help of Internet. It is widespread across multiple domains extending its roots from civil to defense sectors. Although it has deepened its roots, it has certain shortcomings associated with it such as limited storage space, limited processing capability, scheduling complex applications. Large complex applications are normally represented by workflows. A lot of workflow scheduling algorithms are prevailing but somehow each one is having certain issues associated with them. In this paper, we have presented a new workflow scheduling algorithm, i.e., PPSO which is a hybrid combination of heuristic technique, i.e., Predict Earliest Finish Time (PEFT) and meta-heuristic technique, i.e., Particle Swarm Optimization (PSO). The proposed approach is analyzed for different workflows on WorkflowSim simulator. The overall outcomes validates that it outperforms better than existing algorithms.


Cloud computing Internet of Things Particle swarm optimization Predict earliest finish time Workflow scheduling 


  1. 1.
    Al-Roomi, M., Al-Ebrahim, S., Buqrais, S., Ahmad, I.: Cloud computing pricing models: a survey. Int. J. Grid Distrib. Comput. 6(5), 93–106 (2013)CrossRefGoogle Scholar
  2. 2.
    Kavitha, K.: Study on cloud computing models and its benefits, challenges. Int. J. Innov. Res. Comput. Commun. Eng. 2(1), 2423–2431 (2014)Google Scholar
  3. 3.
    Aazam, M., Khan, I., Alsaffar, A.A.: Cloud of things: integrating internet of things and cloud computing and the issues involved. In: Proceeding of 11th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Pakistan (2014).
  4. 4.
    Gil, Y., Deelman, E., Ellisman, M., Moreau, L., Myres, J.: Examining the challenges of scientific workflows. IEEE Comput. 40(12), 26–34 (2007)CrossRefGoogle Scholar
  5. 5.
    Taylor, I., Deelaman, E., Gannon, D., Shields, M.: Workflow for e-Science: Scientific Workflows for Grid, 1st edn. Springer, Berlin (2007)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Mandal, A., Koebill, C., Cooper, K.: Combined fault tolerance and scheduling techniques for workflow applications on computational grid. In: 9th IEEE/ACM International Symposium on Clustering and Grid, pp. 244–251 (2009)Google Scholar
  7. 7.
    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
  8. 8.
    Bardsiri, A.K., Hashemi, S.M.: A review of workflow scheduling in cloud computing environment. Int. J. Comput. Sci. Manag. Res. (IJCSMR) 1(3) (2012)Google Scholar
  9. 9.
    Rahman, M., Hassan, R., Buyya, R.: Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr. Comput.: Pract. Exp. 25(13), 1816–1842 (2013). Scholar
  10. 10.
    Wahab, M.N.A., Meziani, S.N., Tyabi, A.A.: A comprehensive review of swarm optimization algorithms. PLoS J. (2015). Scholar
  11. 11.
    Kachivichyanukul, V.: Comparison of three evolutionary algorithms: GA, PSO and DE. Ind. Eng. Manag. Syst. 11(3), 215–223Google Scholar
  12. 12.
    Arabnejad, H., Barbosa, J.G.: List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans. Parallel Distrib. Syst. 25(3), 682–694 (2014)CrossRefGoogle Scholar
  13. 13.
    Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010). Scholar
  14. 14.
    Dave, Y.P., Shelat, A.S., Patel, D.S., Jhaveri, R.H.: Various job scheduling algorithms in cloud computing: a survey. In: International Conference on Information Communication and Embedded Systems (ICICESS). IEEE, Chennai, IndiaGoogle Scholar
  15. 15.
    Arya, L.K., Verma, A.: Workflow scheduling algorithm in cloud environment—a survey. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), Chandigarh, India (2014).
  16. 16.
    Kwok, Y.K., Ahmad, I.: Dynamic critical path scheduling: an effective technique for allocating task graph to multiprocessors. IEEE Trans. Parallel Distrib. Syst. 7(5), 506–521 (1996)CrossRefGoogle Scholar
  17. 17.
    Illavarsan, E., Thambiduraj, P.: Low complexity performance effective task scheduling algorithm for heterogeneous computing environment. J. Comput. Sci. 3(2), 94–103 (2007). Scholar
  18. 18.
    Sharma, N., Tyagi, S., Atri, S.: A survey on heuristic approach on task scheduling in cloud computing. Int. J. Adv. Res. Comput. Sci. 8(3), 260–274 (2002)Google Scholar
  19. 19.
    Li, K.: Analysis of the list scheduling algorithm for precedence constrained parallel tasks. J. Comb. Optim. 3, 73–88 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    El-Rewini, H., Lewis, T.G.: Scheduling parallel program tasks onto arbitrary target machines. J. Parallel Distrib. Comput. 9(2), 138–153 (1990). Scholar
  21. 21.
    Sih, G.C., Lee, E.A.: A compile-time scheduling heuristic for interconnection-constrained heterogeneous processor architecture. IEEE Trans. Parallel Distrib. Syst. 4(2), 175–187 (1993). Scholar
  22. 22.
    Wu, M.Y., Gajski, D.D.: Hypertool: a programming aid for message passing. IEEE Trans. Parallel Distrib. Syst. 1(3), 330–343 (1990)CrossRefGoogle Scholar
  23. 23.
    Topcuoglu, H., Hariri, S., Wu, M.: Task scheduling algorithms for heterogeneous processors. In: Proceeding of 8th Heterogeneous Computing Workshop (HCS), USA, pp. 3–14 (1999).
  24. 24.
    Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance effective and low complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002). Scholar
  25. 25.
    Lin, C., Lu, S.: Scheduling scientific workflows elastically for cloud computing. In: International Conference on Cloud Computing (CLOUD), pp. 746–747. IEEE, Washington (2011)Google Scholar
  26. 26.
    Bala, R., Singh, G.: An improved heft algorithm using multi-criterion resource factors. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5(6), 6958–6963 (2014)Google Scholar
  27. 27.
    Dubey, K., Kumar, M., Sharma, S.C.: Modified heft algorithm for task scheduling in cloud environment. In: 6th International Conference on Smart Computing and Communications (ICSCC), Kurukshetra, India, pp. 725–732 (2017)Google Scholar
  28. 28.
    Verma, A., Kaushal, S.: Cost-time efficient scheduling plan for executing workflows in the cloud. J. Grid Comput. 13(4), 495–506 (2015)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Rini, D.P., Shamsuddin, S.M., Yuchaniz, S.S.: Particle swarm optimization technique, system and challenges. Int. J. Comput. Appl. (IJCA) 14(1), 19–27Google Scholar
  30. 30.
    Li, D., Shi, H., Liu, J., Tan, S., Liu, C., Xie, Y.: Research on improved particle swarm optimization algorithm based on ant-colony-optimization algorithm. In: 29th Chinese Control and Decision Conference (CCDC), China, pp. 853–858 (2017)Google Scholar
  31. 31.
    Verma, A., Kaushal, S., Sangaiah, A.K.: Computational intelligence based heuristic approach for maximizing energy efficiency in internet of things. In: Intelligent Decision Support Systems for Sustainable Computing, vol. 705, pp. 53–76. Springer, Berlin (2017). Scholar
  32. 32.
    Kaur, G., Kalra, M.: Deadline constrained scheduling of scientific workflows on cloud using hybrid genetic algorithm. In: IEEE Conference, Noida, pp. 281–285 (2017).
  33. 33.
    Li, Z., Liu, X., Duan, X.: Comparative research on particle swarm optimization and genetic algorithm. Comput. Inf. Sci. (CCSE) 3(1), 120–127 (2010)Google Scholar
  34. 34.
    Verma, A., Kaushal, S.: Deadline and budget distribution based cost-time optimization workflow scheduling algorithm for cloud. In: IJCA Proceeding of international conference on Recent Advances and Future Trends in IT, Patiala, India, pp. 1–4 (2012)Google Scholar
  35. 35.
    Smanchat, S., Viriyapant, K.: Taxonomies of workflow scheduling problem and techniques in the cloud. Futur. Gener. Comput. Syst. 52, 1–2 (2015)CrossRefGoogle Scholar
  36. 36.
    Kwok, Y.K., Ahmad, I.: Benchmarking and comparison of the task graph scheduling algorithms. J. Parallel Distrib. Comput. 65(5), 656–665 (2005)Google Scholar
  37. 37.
    Bai, Q.: Analysis of particle swarm optimization algorithm. Comput. Inf. Sci. 3(1), 180–184 (2010). Scholar
  38. 38.
    Chen, W., Deelman, E.: WorkflowSim: a toolkit for simulating scientific workflows in distributed environments. In: IEEE 8th International Conference on E-Science, USA, pp. 1–8 (2012).
  39. 39.
    Sharma, V., Kumar, R.: A survey of energy aware scientific workflows execution techniques in cloud. Int. J. Innov. Res. Comput. Commun. Eng. (IJIRCCE) 3(10), 10336–10343 (2015). Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.U.I.E.T., Panjab UniversityChandigarhIndia

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