Skip to main content
SpringerLink
Log in

Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing is the new technology offering services to build new application through virtualization. Virtualization improves the usage of resource utilization in cloud environment. Recently research in Task Scheduling problem has received more attention because the customers want to maximize the utilization of resources in a cheaper way. In this paper an enhanced particle swarm optimization (PSO) algorithm for improving the efficiency in the task scheduling has been proposed. A ranging function and tuning function based PSO (RTPSO) based on data locality is introduced in this paper for solving the inertia weight assignment problem in existing PSO algorithm for task scheduling. The large inertia weight and small inertia weight will assist a global search and local search respectively. In addition, we have combined the RTPSO with Bat algorithm (RTPSO-B) to improve the optimization. Cloudsim is used in this paper to simulate the task scheduling in cloud environment. The proposed RTPSO-B based task scheduling is compared with various existing task scheduling algorithms such as GA, ACO, ordinary PSO. Simulation results proved proposed RTPSO-B based task scheduling method reduces makespan, cost and increases the utilization of resources.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

References

  1. Bitam, S.: Bees life algorithm for job scheduling in cloud computing. In: Proceedings of The Third International Conference on Communications and Information Technology, pp. 186–191 (2012)

  2. Han, H., Deyui, Q., Zheng, W., Bin, F.: A Qos Guided task scheduling model in cloud computing environment. In: 2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies (EIDWT), IEEE, pp. 72–76 (2013)

  3. Yang, J., Jiang, B., Lv, Z., Choo, K.K.R.: A task scheduling algorithm considering game theory designed for energy management in cloud computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2017.03.024 (2017)

  4. Kumari, K.R., Sengottuvelan, P., Shanthini, J.: A hybrid approach of genetic algorithm and multi objective PSO task scheduling in cloud computing. Asian J. Res. Soc. Sci. Humanit. 7(3), 1260–1271 (2017)

    Google Scholar 

  5. Gabi, D., Ismail, A.S., Zainal, A., Zakaria, Z.: Quality of service (QoS) task scheduling algorithm with taguchi orthogonal approach for cloud computing environment. In: International Conference of Reliable Information and Communication Technology. Springer, Cham, pp. 641–649 (2017)

  6. Goyal, M., Aggarwal, M.: Optimize workflow scheduling using hybrid ant colony optimization (ACO) & particle swarm optimization (PSO) algorithm in cloud environment. Int. J. Adv. Res. Ideas Innov. Technol. 3(2) (2017)

  7. Alkayal, E.S., Jennings, N.R., Abulkhair, M.F.: Efficient task scheduling multi-objective particle swarm optimization in cloud computing. In: 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), IEEE, pp. 17–24 (2016)

  8. Alla, H. B., Alla, S. B., Ezzati, A., Touhafi, A.: An efficient dynamic priority-queue algorithm based on AHP and PSO for task scheduling in cloud computing. In: International Conference on Hybrid Intelligent Systems. Springer, Cham, pp. 134–143 (2016)

  9. Alla, H.B., Alla, S.B., Ezzati, A., Mouhsen, A.: A novel architecture with dynamic queues based on fuzzy logic and particle swarm optimization algorithm for task scheduling in cloud computing. In: Advances in Ubiquitous Networking 2. Springer, Singapore, pp. 205–217 (2017)

  10. Wu, X., Deng, M., Zhang, R., Zeng, B., Zhou, S.: A task scheduling algorithm based on QoS-driven in cloud computing. Proc. Comput. Sci. 17, 1162–1169 (2013)

    Article  Google Scholar 

  11. Alla, H.B., Alla, S.B., Ezzati, A.: A novel architecture for task scheduling based on Dynamic Queues and Particle Swarm Optimization in cloud computing. In: 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech). IEEE, pp. 108–114 (2016)

  12. Gupta, R., Gajera, V., Jana, P.K.: An effective multi-objective workflow scheduling in cloud computing: a PSO based approach. In: 2016 Ninth International Conference on Contemporary Computing (IC3). IEEE, pp. 1–6 (2016)

  13. Verma, A., Kaushal, S.: Bi-criteria priority based particle swarm optimization workflow scheduling algorithm for cloud. In: 2014 Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–6 (2014)

  14. Sahar, M., Vahid, Rafe: A hybrid heuristic workflow scheduling algorithm for cloud computing environments. J. Exp. Theor. Artif. Intell. 27(6), 721–735 (2015)

    Article  Google Scholar 

  15. Jiang, T., Li, J.: Research on the task scheduling algorithm for cloud computing on the basis of particle swarm optimization. Int. J. Simul. https://doi.org/10.5013/IJSSST.a.17.04.11 (2016)

  16. Graham, R.: Static multi-processor scheduling with ant colony optimisation and local search. Master of Science thesis, University of Edinburgh, pp. 1–101 (2003)

  17. Page, A.J., Naughton, T.J.: Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing. In: Proceedings of tech 19th Dynamic Task Scheduling with Load, IEEE/ACM International Parallel and Distributed Processing Symposium, pp. 1530–2075 (2005)

  18. Jamali, S., Alizadeh, F., Sadeqi, S.: Task scheduling in cloud computing using particle swarm optimization. In: The Book of Extended Abstracts, 192 (2016)

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

    Article  Google Scholar 

  20. 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 

  21. Awadalla, M., Elewi, A.: Enhanced PSO approach for real time systems scheduling. Int. J. Comput. Theory Eng. 8(4), 285 (2016)

    Article  Google Scholar 

  22. Li, H.H., Fu, Y.W., Zhan, Z.H., Li, J.J.: Renumber strategy enhanced particle swarm optimization for cloud computing resource scheduling. In: 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, pp. 870–876 (2015)

  23. Priyadarsini, R.J., Arockiam, L.: An improved particle swarm optimization algorithm for meta task scheduling in cloud environment. Int. J. Comput. Sci. Trends Technol. (IJCST) 3(4), 108–112 (2015)

    Google Scholar 

  24. Manogaran, G., Thota, C., Kumar, M.V.: Meta cloud data storage architecture for big data security in cloud computing. Proc. Comput. Sci. 87, 128–133 (2016)

    Article  Google Scholar 

  25. Dubey, I., Gupta, M.: Enhanced particle swarm optimization with uniform mutation and SPV rule for grid task scheduling. Int. J. Comput. Appl. 116(15), 14–17 (2015)

    Google Scholar 

  26. Masdari, M., Salehi, F., Jalali, M., Bidaki, M.: A survey of PSO-based scheduling algorithms in cloud computing. J. Netw. Syst. Manage. 25, 122–158 (2016)

    Article  Google Scholar 

  27. Zhao, C., Zhang, S., Liu, Q., Xie, J., Hu, J.: Independent tasks scheduling based on genetic algorithm in cloud computing. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009. WiCom’09. IEEE, pp. 1–4 (2009)

  28. Hu, B., Sun, X., Li, Y., Sun, H.: An improved adaptive genetic algorithm in cloud computing. In: 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), IEEE, pp. 294–297 (2012)

  29. Patel, S.J., Bhoi, U.R.: Improved Priority Based Job Scheduling Algorithm in Cloud Computing Using Iterative Method. In: 2014 Fourth International Conference on Advances in Computing and Communications (ICACC), IEEE, pp. 199–202 (2014)

  30. Fang, Y., Wang, F., Ge, J.: A task scheduling algorithm based on load balancing in cloud computing. In: International Conference on Web Information Systems and Mining. Springer, Berlin, pp. 271–277 (2010)

  31. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  32. Potluri, S., Rao, K.S.: Quality of service based task scheduling algorithms in cloud computing. Int. J. Electr. Comput. Eng. (IJECE) 7(2), 1088–1095 (2017)

    Article  Google Scholar 

  33. Xu, B., Zhao, C., Hu, E., Hu, B.: Job scheduling algorithm based on Berger model in cloud environment. Adv. Eng. Softw. 42(7), 419–425 (2011)

    Article  Google Scholar 

  34. Goyal, T., Agrawal, A.: Host scheduling algorithm using genetic algorithm in cloud computing environment. Int. J. Res. Eng. Technol. (IJRET) 1, 7–12 (2013)

    Google Scholar 

  35. Li, K., Xu, G., Zhao, G., Dong, Y., Wang, D.: Cloud task scheduling based on load balancing ant colony optimization. In: 2011 Sixth Annual on Chinagrid Conference (ChinaGrid), IEEE, pp. 3–9 (2011)

  36. Choudhary, M., Peddoju, S.K.: A dynamic optimization algorithm for task scheduling in cloud environment. Int. J. Eng. Res. Appl. (IJERA) 2(3), 2564–2568 (2012)

    Google Scholar 

  37. Ergu, D., Kou, G., Peng, Y., Shi, Y., Shi, Y.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64, 1–14 (2013)

    Article  Google Scholar 

  38. Bhoi, U., Ramanuj, P.N.: Enhanced max-min task scheduling algorithm in cloud computing. Int. J. Appl. Innov. Eng. Manag. 2(4), 259–64 (2013)

    Google Scholar 

  39. Fang, Y., Wang, F., Ge, J.: A task scheduling algorithm based on load balancing in cloud computing. In: International Conference on Web Information Systems and Mining. Springer, Berlin, pp. 271–277 (2010)

  40. Jin, J., Luo, J., Song, A., Dong, F., Xiong, R.: Bar: an efficient data locality driven task scheduling algorithm for cloud computing. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, pp. 295–304 (2011)

  41. Ge, H.-W., Sun, L., Liang, Y.-C., Qian, F.: An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling. IEEE Trans. Syst. Man Cybern. 38, 358–368 (2008)

    Article  Google Scholar 

  42. Qin, X., Yang, Z., Li, W., Yang, Y.: Optimized task scheduling and resource allocation in cloud computing using PSO based fitness function. Inf. Technol. J. 12, 7090–7095 (2013)

    Article  Google Scholar 

  43. Li, Z., Wang, C., Lv, H., Xu, T.: Application of PSO algorithm based on improved accelerating convergence in task scheduling of cloud computing environment. Int. J. Grid Distrib. Comput. 9(9), 269–280 (2016)

    Article  Google Scholar 

  44. Xue, S., Shi, W., Xu, X.: A heuristic scheduling algorithm based on PSO in the cloud computing environment. Int. J. u- e-Serv. Sci. Technol. 9(1), 349–362 (2016)

    Article  Google Scholar 

  45. Ramezani, F., Lu, J., Hussain, F.K.: Task-based system load balancing in cloud computing using particle swarm optimization. Int. J. Parallel Program. 42(5), 739–754 (2014)

    Article  Google Scholar 

  46. Liu, L., Zhang, M., Lin, Y., Qin, L.: A survey on workflow management and scheduling in cloud computing. In: 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, pp. 837–846 (2014)

  47. Shaw, S.B., Singh, A.K.: A survey on scheduling and load balancing techniques in cloud computing environment. In: 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, pp. 87–95 (2014)

  48. Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P.P., Kolodziej, J., Balaji, P., Khan, S.U.: A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing 98(7), 751–774 (2016)

    Article  MathSciNet  Google Scholar 

  49. Abdullahi, M., Ngadi, M.A.: Symbiotic organism search optimization based task scheduling in cloud computing environment. Future Gener. Comput. Syst. 56, 640–650 (2016)

    Article  Google Scholar 

  50. Guo, L., Zhao, S., Shen, S., Jiang, C.: Task scheduling optimization in cloud computing based on heuristic algorithm. J. Netw. 7, 547–553 (2012)

    Google Scholar 

  51. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of Conference on Evolutionary Computation (CEC), pp. 1942–1948 (1995)

  52. Varatharajan, R., Manogaran, G., Priyan, M.K., Sundarasekar, R.: Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm. Cluster Comput. https://doi.org/10.1007/s10586-017-0977-2 (2017)

  53. Varatharajan, R., Manogaran, G., Priyan, M.K., Balaş, V.E., Barna, C.: Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimed. Tools Appl. https://doi.org/10.1007/s11042-017-4768-9 (2017)

  54. Varatharajan, R., Vasanth, K., Gunasekaran, M., Priyan, M., Gao, X.Z.: An adaptive decision based kriging interpolation algorithm for the removal of high density salt and pepper noise in images. Comput. Electr. Eng. https://doi.org/10.1016/j.compeleceng.2017.05.035 (2017)

  55. Valarmathi, R., Sheela, T.: A comprehensive survey on task scheduling for parallel workloads based on particle swarm optimization under cloud environment. In: Second IEEE International Conference on Computing and Communications Technologies (ICCCT) (2017)

  56. Valarmathi, R., Sheela, T.: A novel hierarchical scheduling method for managing parallel workloads in cloud. Glob. J. Pure Appl. Math. 12(2), 1647–1662 (2016)

    Google Scholar 

  57. Shen, X., Chi, Z., Yang, J., Chen, C.: Particle swarm optimization with dynamic adaptive inertia weight. In: 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE), IEEE, vol. 1, pp. 287–290 (2010)

  58. Syed, H., Adil, K., Raza, U., Ahmed, S.S., Azhar, A., Masoor, H.: Cloud task scheduling using nature inspired meta-heuristic algorithm. In: International Conference on Open Source Systems and Technologies (ICOSST), IEEE (2015)

  59. Yang, X.-S.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  60. Al-maamari, A., Omara, Fatma A.: Task scheduling using hybrid algorithm in cloud computing environments: IOSR. J. Comput. Eng. (IOSR-JCE) 17(3), 96–106 (2015)

    Google Scholar 

  61. Gomathi, B., Krishnasamy, K.: Task scheduling algorithm based on hybrid particle swarm optimisation in cloud computing environment. J. Theor. Appl. Inf. Technol. 55, 33–38 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

    R. Valarmathi

  2. Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, Tamilnadu, India

    R. Valarmathi

  3. Department of Information Technology, Sri Sairam Engineering College, Chennai, Tamil Nadu, India

    T. Sheela

Authors
  1. R. Valarmathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Sheela
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Valarmathi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valarmathi, R., Sheela, T. Ranging and tuning based particle swarm optimization with bat algorithm for task scheduling in cloud computing. Cluster Comput 22 (Suppl 5), 11975–11988 (2019). https://doi.org/10.1007/s10586-017-1534-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1534-8

Keywords

Search

Navigation