Skip to main content
SpringerLink
Log in

Meta-heuristic based framework for workflow load balancing in cloud environment

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Cloud services are based on datacenter which provides resources on demand with higher capacity, lowest response time and improved resource utilization. The data center comprises of physical hosts which are effectively utilized in the form of Virtual Machines. The task scheduling problem is the mapping of tasks to suitable resources (VMs) as required and it is NP-hard problem. Further, the scheduling algorithms are followed by load balancing techniques for efficient utilization of VMs. In this paper a framework for Load balancing in Cloud Environment has been proposed and implemented for overflow and underflow VM identification. Two metaheuristics and one heuristic have been used in the proposed framework to achieve effective and efficient utilization of VMs in cloud environment. Further, the performance of the proposed framework has been analysed on the basis of makespan and cost metrics which are computed while executing scientific workflow tasks.

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

Similar content being viewed by others

References

  1. Prajapati HB, Shah VA (2014) Scheduling in grid computing environment. In: Proceeding of 4th international conference on advanced computing and communication technologies (ACCT), IEEE, pp 315–324

  2. Kaur A, Kaur B, Singh D (2017) Optimization techniques for resource provisioning and load balancing in cloud environment: a review. Int J Inf Eng Electron Bus 9(1)

  3. Abrishami S, Naghibzadeh M, Epema DH (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gen Comput Syst 29(1):158–169

    Article  Google Scholar 

  4. Kaur A, Kaur B, Singh D (2017) Particle swarm optimization based dynamic load balancing in cloud environment. In: Proceeding of 11th INDIACom, IEEE 4th international conference on computing for sustainable global development, held at BVICAM, New Delhi, March 2017

  5. Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694

    Article  Google Scholar 

  6. Dhinesh Babu LD, Krishna PV (2013) Honey Bee behavior inspired load balancing of tasks in cloud computing environments. Appl Soft Comput J 13(5):2292–2303

    Article  Google Scholar 

  7. Rahman M, Hassan R, Ranjan R, Buyya R (2013) Adaptive workflow scheduling for dynamic grid and cloud computing environment. Concurr Comput Pract Exp 25(13):1816–1842

    Article  Google Scholar 

  8. Ilavarasan E, Thambidura P (2007) Low complexity performance effective task scheduling algorithm for heterogeneous computing environments. J Comput Sci 3(2):94–103

    Article  Google Scholar 

  9. García-Gonzalo E, Fernández-Martínez J (2012) A brief historical review of particle swarm optimization (PSO). J Bioinf Intell Control 1(1):3–16

    Article  Google Scholar 

  10. Devi DC, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for non-preemptive dependent tasks. Sci World J 216:1–14

    Article  Google Scholar 

  11. Ahmad SG, Liew CS, Munir EU, Ang TF, Khan SU (2016) A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems. J Parallel Distrib Comput 87:80–90

    Article  Google Scholar 

  12. Santos D, de Sousa A, Alvelos F (2013) A hybrid column generation with GRASP and path relinking for the network load balancing problem. Comput Oper Res 40(12):3147–3158

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  14. Bharathi S, Chervenak A, Deelman E (2008) Characterization of scientific workflows. In: Third workshop on workflows in support of large-scale science (WORKS08)

  15. Kumar Dinesh (2017) Feature selection for face recognition using DCT-PCA and bat algorithm. Int J Inf Technol 9(4):411–423

    Google Scholar 

  16. Tripathi A, Shukla S, Arora D (2018) A hybrid optimization approach for load balancing in cloud computing. In: Advances in computer and computational sciences. Springer, Singapore, pp 197–206

  17. Cho KM, Tsai PW, Tsai CW, Yang CS (2015) A hybrid meta-heuristic algorithm for VM scheduling with load balancing in cloud computing. Neural Comput Appl 26(6):1297–1309

    Article  Google Scholar 

  18. Aruna M, Bhanu D, Karthik S (2017) An improved load balanced metaheuristic scheduling in cloud. Cluster Comput 1–9

Download references

Acknowledgements

I am extremely thankful to my advisor and guide. Dr. Bikrampal Kaur, Professor, CEC, under whose guidance and valuable suggestions, I am able to complete this paper. I am highly grateful to IKGPTU, Jalandhar and CEC, Landran for providing me the platform for undergoing my Ph.D research work.

Author information

Authors and Affiliations

  1. IKGPTU, Jalandhar, India

    Amanpreet Kaur

  2. Chandigarh Engineering College, Landran, Mohali, India

    Bikrampal Kaur

  3. CCET, Chandigarh, India

    Dheerendra Singh

Authors
  1. Amanpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bikrampal Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dheerendra Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amanpreet Kaur.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, A., Kaur, B. & Singh, D. Meta-heuristic based framework for workflow load balancing in cloud environment. Int. j. inf. tecnol. 11, 119–125 (2019). https://doi.org/10.1007/s41870-018-0231-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-018-0231-z

Keywords

Search

Navigation