Skip to main content

Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 424)


Cloud computing is a promising paradigm which provides resources to customers on their request with minimum cost. Cost effective scheduling and load balancing are major challenges in adopting cloud computation. Efficient load balancing methods avoids under loaded and heavy loaded conditions in datacenters. When some VMs are overloaded with several number of tasks, these tasks are migrated to the under loaded VMs of the same datacenter in order to maintain Quality of Service (QoS). This paper proposes a modification in the bee colony algorithm for efficient and effective load balancing in cloud environment. The honey bees foraging behaviour is used to balance load across virtual machines. The tasks removed from over loaded VMs are treated as honeybees and under loaded VMs are the food sources. The method also tries to minimize makespan as well as number of VM migrations. The experimental result shows that there is significant improvement in the QoS delivered to the customers.


  • Cloud computing
  • Task scheduling
  • Bee colony algorithm
  • Load balancing
  • Qos

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Springer, J. Glob. Optim. 39, 459–471 (2007)

    Google Scholar 

  2. Ajit, M., Vidya, G.: VM level load balancing in cloud environment.: In: IEEE Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5 (2013)

    Google Scholar 

  3. Fahim, Y., Ben Lahmar, E., El Labrlji, E.H., Eddaoui, A.: The load balancing based on the estimated finish time of tasks in cloud computing. In: 2nd World Conference on Complex Systems (WCCS), pp. 594–598 (2014)

    Google Scholar 

  4. Remesh Babu, K.R., Mathiyalagan, P., Sivanandam, S.N.: Pareto-Pareto based hybrid Meta heuristic ABC—ACO approach for task scheduling in computational grids. Int. J. Hybrid Intell. Syst. 11(4/2014), 241–255 (2014)

    Google Scholar 

  5. Madivi, R., Kamath, S.S.: An hybrid bio-inspired task scheduling algorithm in cloud environment. In: International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1 –7 (2014)

    Google Scholar 

  6. Wang, L., Zhou, G., Xu, Y., Liu, M.: An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int. J. Adv. Manuf. Technol. 60(Issue 9–12), 1111–1123. Springer (2012)

    Google Scholar 

  7. Domanal, S.G.R., Ram Mohana, G.: Load balancing in cloud computing using modified throttled algorithm. In: IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), pp. 1–5 (2013)

    Google Scholar 

  8. Shridhar, G.D., Reddy, G.R.M.: Optimal load balancing in cloud computing by efficient utilization of virtual machines. In: IEEE Sixth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–4 (2014)

    Google Scholar 

  9. Sharma, A., Peddoju, S.K.: Response time based load balancing in cloud computing. In: International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1287–1293 (2014)

    Google Scholar 

  10. Soni, G., Kalra, M.: A novel approach for load balancing in cloud data center. In: IEEE International Conference on Advance Computing Conference (IACC), pp. 807–812 (2014)

    Google Scholar 

  11. Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: An ant colony based load balancing strategy in cloud computing. Springer Advanced Computing, Networking and Informatics, Vol. 28, pp. 403–413 (2014)

    Google Scholar 

  12. Mohammadreza, M., Amir, M.R., Anthony, T.C.: Cloud light weight: a new solution for load balancing in cloud computing. In: International Conference on Data Science and Engineering (ICDSE), pp. 44–50 (2014)

    Google Scholar 

  13. Chen, L., Shen, H., Sapra, K.: RIAL: resource intensity aware load balancing in clouds. In: IEEE Conference on Computer Communications (INFOCOM), pp. 1294–1302 (2014)

    Google Scholar 

  14. Xu, G., Pang, J., Fu, X.: A load balancing model based on cloud partitioning for the public cloud. In: IEEE Journal of Tsinghua Science and Technology, pp. 34–39 (2013)

    Google Scholar 

  15. Randles, M., Lamb, D., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. In: Proceedings of the IEEE 24th International Conference on Advanced Information Networking and Applications, Perth, Australia, pp. 551–556 (2010)

    Google Scholar 

  16. Yao, J., He, J.: Load balancing strategy of cloud computing based on artificial bee algorithm. In: IEEE 8th International Conference on Computing Technology and Information Management (ICCM), pp. 185–189 (2012)

    Google Scholar 

  17. Samal, P.: Analysis of variants in Round Robin Algorithms for load balancing in cloud computing. Int. J. Comput. Sci. Inf. Technol. 4(3), 416–419 (2013)

    Google Scholar 

  18. Remesh Babu, K.R., Samuel, P.: Virtual machine placement for improved quality in IaaS cloud. In: IEEE Fourth International Conference on Advances in Computing and Communications (ICACC), pp. 190–194 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to K. R. Remesh Babu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Remesh Babu, K.R., Samuel, P. (2016). Enhanced Bee Colony Algorithm for Efficient Load Balancing and Scheduling in Cloud. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28030-1

  • Online ISBN: 978-3-319-28031-8

  • eBook Packages: EngineeringEngineering (R0)