Skip to main content

Advertisement

SpringerLink
Log in

Task scheduling in a cloud computing environment using HGPSO algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. Optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-Particle Swarm Optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the on-demand queue.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. 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. Future Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  2. Mocanu, E.M., Florea, M., Andreica, M.I., Ţăpuş, N.: Cloud computing—task scheduling based on genetic algorithms. In: IEEE System Conference (Syscon), pp. 1–6 (2012)

  3. Ghanbari, S., Othman, M.: A priority based job scheduling algorithm in cloud computing. Procedia Eng. 50, 778–785 (2012)

    Article  Google Scholar 

  4. Premalatha, K., Natarajan, A.M.: Hybrid PSO and GA for global maximization. Int. J. Open Probl. Comput. Sci. Math. 2(4), 597–608 (2009)

    MathSciNet  Google Scholar 

  5. Kaveh, A., Malakouti Rad, S.: Hybrid genetic algorithm and particle swarm optimization for the force method-based simultaneous analysis and design. Iran. J. Sci. Technol. Trans. B. 34, 15–34 (2010)

    Google Scholar 

  6. Alejandra Rodriguez, M., Buyya, R.: Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds. IEEE Trans. Cloud Comput. 2(2), 222–235 (2014)

    Article  Google Scholar 

  7. Mudjihartono, P., Setthawong, R., Tanprasert, T.: Parallelized GA-PSO algorithm for solving Job Shop Scheduling Problem. In: 2nd International Conference on Science in Information Technology (ICSITech), pp. 103–108 (2016)

  8. Meng, Q., Zhang, L., Fan, Y.: A hybrid particle swarm optimization algorithm for solving job shop scheduling problems. In: Zhang, L., Song, X., Wu, Y. (eds.) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2016, SCS AutumnSim 2016, vol. 644, pp. 71–78. Communications in Computer and Information Science. Springer, Singapore (2016)

  9. Manasrah, A.M., Ali, H.B.: Workflow scheduling using hybrid GA-PSO algorithm in cloud computing. Wirel. Commun. Mob. Comput. 2018, 1–16 (2018)

    Article  Google Scholar 

  10. Kamalinia, A., Ghaffari, A.: Hybrid task scheduling method for cloud computing by genetic and PSO algorithms. J. Inf. Syst. Telecommun. 4, 271–281 (2016)

    Google Scholar 

  11. Shyamala, K., Sunitha Rani, T.: An analysis on efficient resource allocation mechanisms in cloud computing. Indian J. Sci. Technol. 8(9), 814–821 (2015)

    Article  Google Scholar 

  12. Chalack, V.A., Razavi, S.N., Gudakahriz, S.J.: Resource allocation in cloud environment using approaches based particle swarm optimization. Int. J. Comput. Appl. Technol. Res. 6(2), 87–90 (2017)

    Google Scholar 

  13. Zeng, Z., Truong-Huu, T., Veeravalli, B., Tham, C.-K.: Operational cost-aware resource provisioning for continuous write applications in cloud-of-clouds. Clust. Comput. 19, 1–14 (2016)

    Article  Google Scholar 

  14. Sontakke, V., Patil, P., Waghamare, S., Kulkarni, R., Patil, N.S., Saravanapriya, M.: Dynamic resource allocation strategy for cloud computing using virtual machine environment. Int. J. Eng. Sci. Comput. 6(5), 4804–4806 (2016)

    Google Scholar 

  15. Singh, S., Chana, I.: Q-aware: quality of service based cloud resource provisioning. Comput. Electr. Eng. 47, 138–160 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, Tejaa Shakthi Institute of Technology for Women, Coimbatore, Tamil Nadu, India

    A. M. Senthil Kumar

  2. KSR Institute for Engineering and Technology, Tiruchengode, Tamil Nadu, India

    M. Venkatesan

Authors
  1. A. M. Senthil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Venkatesan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. M. Senthil Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senthil Kumar, A.M., Venkatesan, M. Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Comput 22 (Suppl 1), 2179–2185 (2019). https://doi.org/10.1007/s10586-018-2515-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2515-2

Keywords

Search

Navigation