Skip to main content
SpringerLink
Log in

A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred to as load balancing (LB). Therefore, in cloud an active load balancing scheme can perhaps enhance the reliability, services and the utilization of resources as well. In this manuscript, the benefits are integrated for Harries Hawks Optimization and Pigeon inspired Optimization Algorithm to create efficient load balancing scheme, which ensures the optimal resources utilizations with tasks response time. The proposed approach is implemented in JAVA Net beans IDE incorporated in the cloudsim framework that is analyzed based on different number of task in order to assess the performance. However, the simulation outcomes demonstrate that the proposed Hawks Optimization and Pigeon inspired Optimization algorithm based load balancing scheme is significantly balance the load optimally amid the Virtual Machines within a shorter period of time than the existing algorithms. The efficiency of the proposed method is 97% compared to the other existing methods. The computational time, cost, throughput analysis, make span, latency, execution time are determined and gets analysed, compared with the Harries Hawks Optimization, Spider Monkey Algorithm, Ant Colony Optimization and Honey Bee Optimization.

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

Similar content being viewed by others

References

  1. Miyachi, C.: What is “cloud”? It is time to update the NIST definition? IEEE Cloud Comp. 5, 6–11 (2018)

    Google Scholar 

  2. Tsai, C., Rodrigues, J.: Meta-heuristic scheduling for cloud: a survey. IEEE Syst. J. 8, 279–291 (2014)

    Article  Google Scholar 

  3. Mishra, S., Puthal, D., Sahoo, B., Jena, S., Obaidat, M.: An adaptive task allocation technique for green cloud computing. J. Supercomput. 74, 370–385 (2017)

    Article  Google Scholar 

  4. Ibrahim, A., Faheem, H., Mahdy, Y., Hedar, A.: Resource allocation algorithm for GPUs in a private cloud. Int. J. Cloud Comput. 5, 45 (2016)

    Article  Google Scholar 

  5. Jebalia, M., Letaïfa, A., Hamdi, M., Tabbane, S.: An overview on coalitional game-theoretic approaches for resource allocation in cloud computing architectures. Int. J. Cloud Comput. 4, 63 (2015)

    Article  Google Scholar 

  6. Maheswaran, M., Ali, S., Siegel, H., Hensgen, D., Freund, R.: Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J. Parallel Distrib. Comput. 59, 107–131 (1999)

    Article  Google Scholar 

  7. Singh, A., Juneja, D., Malhotra, M.: A novel agent based autonomous and service composition framework for cost optimization of resource provisioning in cloud computing. J. King Saud Univ. Comput. Inf. Sci. 29, 19–28 (2017)

    Article  Google Scholar 

  8. Van Noorden, R.: The arXiv preprint server hits 1 million articles. Nature. (2014)

  9. Mythili, S., Thiyagarajah, K., Rajesh, P., Shajin, F.H.: Ideal position and size selection of unified power flow controllers (UPFCs) to upgrade the dynamic stability of systems: An antlion optimiser and invasive weed optimisation Algorithm. HKIE Trans. 27, 25–37 (2020). https://doi.org/10.33430/V27N1THIE-2018-0024

    Article  Google Scholar 

  10. Sulaiman, N., Masuda, H.: Evaluation of a secure live migration of virtual machines using IPsec implementation. Int. J. Netw. Distrib. Comput. 3(99) (2015)

  11. Zhu, L., Gu, J., Wang, Y., Zhao, T., Cai, Z.: Optimizing the fault-tolerance overheads of HPC systems using prediction and multiple proactive actions. J. Supercomput. 71, 3668–3694 (2015)

    Article  Google Scholar 

  12. Hsiao, H., Chung, H., Shen, H., Chao, Y.: Load rebalancing for distributed file Systems in Clouds. IEEE Trans. Parallel Distrib. Syst. 24, 951–962 (2013)

    Article  Google Scholar 

  13. Ballani, H., Costa, P., Karagiannis, T., Rowstron, A.: Towards predictable datacenter networks. ACM SIGCOMM Comput. Commun. Rev. 41, 242–253 (2011)

    Article  Google Scholar 

  14. Kumar, A., Raj, A.: A new static load balancing algorithm in cloud computing. Int. J. Comput. Appl. 132, 13–18 (2015)

    Google Scholar 

  15. Li, K.: Optimal load distribution for multiple heterogeneous blade servers in a cloud computing environment. J. Grid Comput. 11(1), 27–46 (2013)

    Article  Google Scholar 

  16. Mu, S.: Task scheduling optimization algorithm based on load balance under the cloud computing environment. Int. J. Appl. Decis. Sci. 11, 1 (2018)

    Google Scholar 

  17. Patni, J., Aswal, M.: Distributed approach of load balancing in dynamic grid computing environment. Int. J. Commun. Netw. Distrib. Syst. 19, 1 (2017)

    Google Scholar 

  18. Kashyap, D., Viradiya, J.: A review on various approaches of load balancing in cloud computing. Int. J. Sci. Res. (IJSR). 5, 868–871 (2016)

    Google Scholar 

  19. Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput. 14(2), 217–264 (2016)

    Article  Google Scholar 

  20. Deng, Y., Lau, R.: Dynamic load balancing in distributed virtual environments using heat diffusion. ACM Trans. Multimed. Comput. Commun. Appl. 10, 1–19 (2014)

    Article  Google Scholar 

  21. Jansen, K., Land, F., Land, K.: Bounding the running time of algorithms for scheduling and packing problems. SIAM J. Discret. Math. 30, 343–366 (2016)

    Article  MathSciNet  Google Scholar 

  22. Peng, J., Tang, M., Li, M., Zha, Z.: A load balancing method for massive data processing under cloud computing environment. Intell. Autom. Soft Comput. 23, 547–553 (2017)

    Article  Google Scholar 

  23. Lin, W., Peng, G., Bian, X., Xu, S., Chang, V., Li, Y.: Scheduling algorithms for heterogeneous cloud environment: Main resource load balancing algorithm and time balancing algorithm. J. Grid Comput. 17(4), 699–726 (2019)

    Article  Google Scholar 

  24. Vasudevan, S. K., Anandaram, S., Menon, A. J., and Aravinth, A.: Honey bee based load balancing in cloud computing. KSII Trans. Internet Inf. Syst. 11, (2017)

  25. Mukati, L., Upadhyay, A.: A survey on static and dynamic load balancing algorithms in cloud computing. SSRN Electron. J. (2019)

  26. Ping, Y.: Load balancing algorithms for big data flow classification based on heterogeneous computing in software definition networks. J. Grid Comput. 15, 1–7 (2020). https://doi.org/10.1007/s10723-020-09511-5

    Article  MathSciNet  Google Scholar 

  27. Lavanya, M., Vaithiyanathan, V.: Load prediction algorithm for dynamic resource allocation. Indian J. Sci. Technol. 8, (2015)

  28. N, M.: Task-based system load balancing in cloud computing using particle swarm optimization. Indian J. Sci. Technol. 8, (2015)

  29. Kalra, M., Singh, S.: An intelligent water drops-based approach for workflow scheduling with balanced resource utilisation in cloud computing. Int. J. Grid Util. Comput. 10(528), 528 (2019)

    Article  Google Scholar 

  30. Narang, A., Laxmi, V.: A review on various approaches of load balancing in cloud computing. Int. J. Sci. Res. (IJSR). 5, 868–871 (2016)

    Google Scholar 

  31. L.D., D., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13, 2292–2303 (2013)

  32. Mansourkiaie, F., Ahmed, M.: Per-node traffic load in cooperative wireless sensor networks. IEEE Commun. Lett. 20, 344–347 (2016)

    Article  Google Scholar 

  33. Alam, M., Pandey, M., Rautaray, S.: A comprehensive survey on cloud computing. Int. J. Inf. Technol. Comput. Sci. 7, 68–79 (2015)

    Google Scholar 

  34. Heidari, A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., Chen, H.: Harris hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  35. Cui, Z., Zhang, J., Wang, Y., Cao, Y., Cai, X., Zhang, W., Chen, J.: A pigeon-inspired optimization algorithm for many-objective optimization problems. SCIENCE CHINA Inf. Sci. 62, 70212–70211 (2019)

    Article  Google Scholar 

  36. Transpire Online: Dolphin Echolocation Algorithm (DEA): Pigeon Inspired Optimization (PIO) Algorithm: A Novel method motivated from the behavior of Pigeons for Optimal Solution, Transpire Online 2019. Available at: https://transpireonline.blog/tag/pigeon-inspired-optimization/. Accessed on: Nov 2019

  37. Kaur, A.: Efficient cloud server job scheduling using NN and ABC in cloud computing. Int. J. Eng. Comput. Sci. (2016)

  38. Hou, X., Zhao, G.: Resource scheduling and load balancing fusion algorithm with deep learning based on cloud computing. Int. J. Inf. Technol. Web Eng. 13, 54–72 (2018)

    Article  Google Scholar 

  39. Narale, S., Butey, P.: Implementation of load balancing algorithms in cloud computing environment using cloud analyst simulator. Int. J. Recent Trends Eng. Res. 4, 22–27 (2018)

    Google Scholar 

  40. Geetha, P., Robin, C.: Load balancing in cloud computing. Int. J. Recent Trends Eng. Res. 3, 260–267 (2017)

    Article  Google Scholar 

  41. Chunlin, L., Min, Z., Youlong, L.: Efficient load-balancing aware cloud resource scheduling for Mobile user. Comput. J. 60, 925–939 (2017)

    Article  Google Scholar 

  42. Zhao, J., Yang, K., Wei, X., Ding, Y., Hu, L., Xu, G.: A heuristic clustering-based task deployment approach for load balancing using Bayes theorem in cloud environment. IEEE Trans. Parallel Distrib. Syst. 27, 305–316 (2016)

    Article  Google Scholar 

  43. Paya, A., Marinescu, D.: Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Trans. Cloud Comput. 5, 15–27 (2017)

    Article  Google Scholar 

  44. Milan, S.T., Rajabion, L., Ranjbar, H., Navimipour, N.J.: Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments. Comput. Oper. Res. 110, 159–187 (2019)

    Article  MathSciNet  Google Scholar 

  45. Adhikari, M., Nandy, S., Amgoth, T.: Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. J. Netw. Comput. Appl. 128, 64–77 (2019)

    Article  Google Scholar 

  46. Ziyath, S.P.M. and Senthilkumar, S.: MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services. J. Ambient. Intell. Humanized Comput. 1–10 (2020)

  47. Jena, U.K., Das, P.K. and Kabat, M.R.: Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. J. King Saud Univ. –Comput. Inf. Sci. (2020)

  48. Vinothini, C. and Balasubramanie, P.: Meta-heuristic firefly approach to multi-servers load balancing with independent and dependent server availability consideration. Journal of Ambient Intelligence and Humanized Computing. 1–13 (2020)

  49. Attiya, I., Abd Elaziz, M. and Xiong, S.: Job scheduling in cloud computing using a modified harris hawks optimization and simulated annealing algorithm. Comput. Intell. Neurosci. 2020

  50. Golchi, M.M., Saraeian, S., Heydari, M.: A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation. Comput. Netw. 162, 106860 (2019)

    Article  Google Scholar 

  51. Li, G., Wu, Z.: Ant colony optimization task scheduling algorithm for SWIM based on load balancing. Futur. Internet. 11, 90 (2019)

    Article  Google Scholar 

  52. Deng, W., Chen, H., Li, H.: A novel hybrid intelligence algorithm for solving combinatorial optimization problems. J. Comput. Sci. Eng. 8, 199–206 (2014)

    Article  Google Scholar 

  53. Mayilsamy, J. and Rangasamy, D.P.: Load balancing in software-defined networks using spider monkey optimization algorithm for the internet of things. Wirel. Pers. Commun. 1–21 (2020)

  54. Lin, Y.D., Wang, C.C., Lu, Y.J., Lai, Y.C., Yang, H.C.: Two-tier dynamic load balancing in SDN-enabled Wi-Fi networks. Wirel. Netw. 24(8), 2811–2823 (2018)

    Article  Google Scholar 

  55. Polepally, V. and Chatrapati, K.S.: Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust. Comput. 1–13 (2019)

Download references

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, V V College of Engineering, Tisaiyanvilai, Tamil Nadu, India

    G. Annie Poornima Princess

  2. Department of Electronics and Communication Engineering, V V College of Engineering, Tisaiyanvilai, Tamil Nadu, India

    A. S. Radhamani

Authors
  1. G. Annie Poornima Princess
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. S. Radhamani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Annie Poornima Princess.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Annie Poornima Princess, G., Radhamani, A.S. A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing. J Grid Computing 19, 21 (2021). https://doi.org/10.1007/s10723-021-09560-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-021-09560-4

Keywords

Search

Navigation