Cloud computing-based resource provisioning using k-means clustering and GWO prioritization

Abstract

As the use of cloud computing continues to grow, issues related to cloud services such as resource allocation, security, virtual machine migration and quality of service (QoS) are also increasing. To overcome this, resource provisioning and stack adjusting were employed. In this paper, we suggest another approach that allocates resources with least waste and provides the greatest benefit. Here, the customer first submits a request for resources to the resource allocation manager, which forwards this request to the request tuner. It creates the charge and sends this to all resources connected in cloud system with the guide of grouping algorithm. After the GWO algorithm is applied for prioritization. The virtual machines are distributed for resources based on require therefore; the load on the server can be substantially reduced. In addition, allocating resources on virtual machines based on demand achieves a better response time and preparation time.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Ardagna D, Panicucci B, Passacantando M (2012) Generalized NASH equilibria for the service provisioning problem in cloud systems. IEEE Trans Serv Comput 6(4):429–442

    Article  Google Scholar 

  2. Arianyan E, Taheri H, Sharifian S (2015) Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Elsevier J Comput Electr Eng 47:222–240

    Article  Google Scholar 

  3. Bahrpeyma F, Haghighi H, Zakerolhosseini A (2016) A bipolar resource management framework for resource provisioning in cloud’s virtualized environment. Elsevier J Appl Soft Comput 46:487–500

    Article  Google Scholar 

  4. Banu M Uthaya, Subha M (2014) A survey on resource provisioning in cloud. Int J Eng Res Appl 4(2):30–35

    Google Scholar 

  5. Bhavani BH, Guruprasad HS (2014) Resource provisioning techniques in cloud computing environment: a survey. IJRCCT Int J Res Comput Commun Technol 3(3):395–401

    Google Scholar 

  6. Casalicchio E, Silvestri L (2013) Mechanisms for SLA provisioning in cloud-based service providers. Elsevier J Comput Netw 57(3):795–810

    Article  Google Scholar 

  7. Chaisiri S, Lee BS, Niyato D (2012) Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comp 5(2):164–177

    Article  Google Scholar 

  8. Fahmi A, Abdullah S, Amin F, Ali A (2017a) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. Intell Syst. https://doi.org/10.1515/jisys-2017-0083

    Article  Google Scholar 

  9. Fahmi A, Abdullah S, Amin F, Siddque N, Ali A (2017b) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-162007

    Article  Google Scholar 

  10. Fahmi A, Abdullah S, Amin F, Ali A, Ahmad Khan W (2018a) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-18125

    Article  Google Scholar 

  11. Fahmi A, Abdullah S, Amin F, Khan MSA (2018b) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput. https://doi.org/10.1007/s00500-018-3242-6

    Article  MATH  Google Scholar 

  12. Fahmi A, Abdullah S, Amin F, Ali A (2018c) Weighted average rating (war) method for solving group decision making problem using triangular cubic fuzzy hybrid aggregation (tcfha). Punjab Univ J Math 50(1):23–34

    MathSciNet  Google Scholar 

  13. Fahmi A, Abdullah S, Amin F, Ahmed R, Ali A (2018d) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. Intell Fuzzy Syst. https://doi.org/10.3233/JIFS-171567

    Article  Google Scholar 

  14. Ficco M, Esposito C, Palmieri F et al (2016) A coral-reefs and game theory-based approach for optimizing elastic cloud resource allocation. Elsevier J Fut Gener Comput Syst 1–10

  15. Heilig E, Lalla-Ruiz E, Voß S (2016) A cloud brokerage approach for solving the resource management problem in multi-cloud environments. Elsevier J Comput Ind Eng 95:16–26

    Article  Google Scholar 

  16. Huang D, He B, Miao C (2014) A survey of resource management in multi-tier web applications. IEEE Trans Commun Surv Tutor 16(3):1574–1590

    Article  Google Scholar 

  17. Jiang Y, Perng CS, Li T et al (2013) Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans Netw Serv Manag 10(3):312–325

    Article  Google Scholar 

  18. Mann ZA (2015) Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center. Elsevier J Fut Gener Comput Syst 51:1–6

    Article  Google Scholar 

  19. Mei J, Li K, Ouyang A, Li K (2015) A profit maximization scheme with guaranteed quality of service in cloud computing. IEEE Trans Comput 64(11):3064–3078

    MathSciNet  Article  Google Scholar 

  20. Niu S, Zhai J, Ma X et al (2015) Building semi-elastic virtual clusters for cost-effective HPC cloud resource provisioning. IEEE Trans Parallel Distrib Syst 27:1915–1928

    Article  Google Scholar 

  21. Soni A, Hasan M (2017) Time and cost based resource provisioning mechanism in cloud computing. Int J Adv Res Comp Sci 8(5):288–292

    Google Scholar 

  22. Tian W, Zhao Y, Xu M et al (2013) A toolkit for modeling and simulation of real-time virtual machine allocation in a cloud data center. IEEE Trans Autom Sci Eng 12(1):153–161

    Article  Google Scholar 

  23. Vasoya S, Gadhavi L, Bhatia J, Bhavsar M (2016) Resource provisioning strategies in cloud: a survey. Resource 7(2):12–15

    Google Scholar 

  24. Wu L, Garg SK, Versteeg S et al (2013) SLA-based resource provisioning for software-as-a-service applications in cloud computing environments. IEEE Trans Serv Comput 7:465–485

    Article  Google Scholar 

  25. Xiao W, Bao W (2015) Dynamic request redirection and resource provisioning for cloud-based video services under heterogeneous environment. IEEE Trans Parallel Distrib 27(7):1954–1967

    Article  Google Scholar 

  26. Zhang J, Huang H, Wang X (2016) Resource provision algorithms in cloud computing: a survey. J Netw Comput Appl 64:23–42

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to A. Meenakshi.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Communicated by V. Loia.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Meenakshi, A., Sirmathi, H. & Anitha Ruth, J. Cloud computing-based resource provisioning using k-means clustering and GWO prioritization. Soft Comput 23, 10781–10791 (2019). https://doi.org/10.1007/s00500-018-3632-9

Download citation

Keywords

  • Resource provisioning
  • Security
  • Virtual machine migration
  • Quality of services
  • Gray wolf optimization