Skip to main content
SpringerLink
Log in

Competent resource provisioning and distribution techniques for cloud computing environment

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The principal intension of the investigation is to provisioning the resources and effectively allocates them. Initially, the resource are identified and analyzed and then clustered. Cluster the resource using the kernel fuzzy c-means clustering algorithm. After the clustering algorithm, the resources are allocated using resource provider. In the proposed method resource allocation is done with the help of modified cloud resource provisioning algorithm. With the help of optimization technique, the traditional OCRP algorithm is improved. Modified cloud resource provisioning algorithm is selecting the resource with minimum cost using optimization. Here particle swarm optimization algorithm is used to select the optimal resource with minimum cost. Finally the resource provisioner allocates the resource in an effective manner. The enactment of the proposed method is evaluated by means of cost value. The proposed technique is performed with the mighty assistance of the Cloud simulator in the working platform of Java software.

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

Similar content being viewed by others

References

  1. Jewarg, P.B., Patil, J.: Payment minimization and error-tolerant resource allocation for cloud system using equally spread current execution load. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 3(8), 2669–2676 (2014)

    Google Scholar 

  2. Pawar, C.S., Wagh, R.B.: Priority based dynamic resource allocation in cloud computing. In: Proceedings of International Symposium on Cloud and Services Computing, pp. 1–6 (2012)

  3. Saranya, S., Saranya, N.: An efficient resource allocation for improving resource utilization in self organizing clouds. Int. J. Innov. Res. Comput. Commun. Eng. 2(1), 1648–1651 (2014)

    Google Scholar 

  4. Sivaranjani, V., Jayamala, R.: Optimization of workload prediction based on map Reduce frame work in a cloud system. Int. J. Res. Eng. Technol. 3(3), 264–266 (2014)

    Article  Google Scholar 

  5. Pandya, P.P., Bheda, H.A.: Dynamic resource allocation techniques in cloud computing. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2(1), 559–563 (2014)

    Google Scholar 

  6. Al-Sharif, Z.A., Jararweh, Y.: Al-Dahoud, A: ACCRS: autonomic based cloud computing resource scaling. Clust. Comput. 20(3), 2479–2488 (2017)

    Article  Google Scholar 

  7. Dhivya, L., Padmaveni, MsK: Dynamic resource allocation using virtual machines for cloud computing environment. IJREAT Int. J. Res. Eng. Adv. Technol. 2(1), 1–4 (2014)

    Google Scholar 

  8. Goudarzi, H., Pedram, M.: Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems. In: Proceeding of IEEE 4th International Conference on Cloud Computing, pp. 324–331 (2011)

  9. He, C., Lu, Y., Swanson, D.: Matchmaking: a new MapReduce scheduling technique. In: Proceeding of Third IEEE International Conference on Cloud Computing Technology and Science, pp. 40–47 (2011)

  10. Ren, H., Lan, Y., Yin, C.: The load balancing algorithm in cloud computing environment. In: 2nd International Conference on Computer Science and Network Technology, pp. 925–928 (2012)

  11. Zhou, Z., Liu, F., Xu, Y., Zou, R., Xu, H., Lui, J.C.S., Jin, H.: Carbon-aware load balancing for geo-distributed cloud services. In: Proceeding of IEEE 21st International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (2013)

  12. Dou, H., Qi, Y., Wang, P.: Hybrid power control and electricity cost management for distributed internet data centers in cloud computing. In: Proceeding of 10th Web Information System and Application Conference, pp. 394–399 (2013)

  13. Aljohani, A.M., Holton, D.R.W., Awan, I., Alanazi, J.S.: Performance evaluation of local and cloud deployment of web clusters. In: Proceeding of International Conference on Network-Based Information Systems, pp. 244–248 (2011)

  14. Toosi, A.N., Calheiros, R.N., Thulasiram, P.K., Buyya, R.: Resource provisioning policies to increase IaaS provider’s profit in a federated cloud environment. In: Proceedings of IEEE International Conference on High Performance Computing and Communications, Banff, Canada, pp. 279–287 (2011)

  15. Zaman, S., Grosu, D.: A combinatorial auction-based mechanism for dynamic VM provisioning and allocation in clouds. IEEE Trans. Cloud Comput. 1(2), 129–141 (2013)

    Article  Google Scholar 

  16. Di, S., Wang, C.-L.: Dynamic optimization of multi attribute resource allocation in self-organizing clouds. IEEE Trans. Parallel Distrib. Syst. 24(3), 464–478 (2013)

    Article  Google Scholar 

  17. Li, S., Zhou, Y., Jiao, L., Yan, X., Wang, Xin, Lyu, Michael Rung-Tsong: Towards operational cost minimization in hybrid clouds for dynamic resource provisioning with delay-aware optimization. IEEE Trans. Serv. Comput. 8(3), 398–409 (2015)

    Article  Google Scholar 

  18. Zhu, Q., Agrawal, G.: Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Trans. Serv. Comput. 5(4), 497–511 (2012)

    Article  Google Scholar 

  19. Chunlin, L., Jianhang, T., Youlong, L.: Distributed QoS-aware scheduling optimization for resource-intensive mobile application in hybrid cloud. Clust. Comput. 20(1), 1–18 (2017)

    Article  Google Scholar 

  20. Li, W., Zhang, Q., Wu, J., Li, J., Hao, H.: Trust-driven and QoS demand clustering analysis based cloud workflow scheduling strategies. Clust. Comput. 17(3), 1–18 (2014)

    Article  Google Scholar 

  21. Byun, E.-K., Kee, Y.-S., Kim, J.-S., Maeng, S.: Cost optimized provisioning of elastic resources for application workflows. Future Gen. Comput. Syst. 27(8), 1011–1026 (2011)

    Article  Google Scholar 

  22. Iqbal, W., Dailey, M.N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gen. Comput. Syst. 27(6), 871–879 (2011)

    Article  Google Scholar 

  23. Phani Praveen, S., Tulasi, U., Vishnu, B., Yuvakrishna, A.: A new approach for optimizing resource provisioning in cloud computing using OCRP algorithm. Int. J. Comput. Sci. Technol. 1(8), 15–21 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, “Nehru Gardens”, T.M. Palayam, Coimbatore, 641 105, India

    A. Suresh

  2. Department of Electronics and Communication Engineering, Sri Lakshmi Ammaal Engineering College, Chennai, India

    R. Varatharajan

Authors
  1. A. Suresh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Varatharajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Suresh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Suresh, A., Varatharajan, R. Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Comput 22 (Suppl 5), 11039–11046 (2019). https://doi.org/10.1007/s10586-017-1293-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1293-6

Keywords

Search

Navigation