[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

An Improved Efficient Dynamic Load Balancing Scheme Under Heterogeneous Networks in Hybrid Cloud Environment

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

In the rapid development of computer network technology. The cloud computing is a novel technology had become a highly demanded service due to several new challenges to all organizations the advantages of high computing power, cost of services, scalability, accessibility and availability. However, Cloud computing supports virtual machines system is more complex while dispatching variety of tasks to server’s applications simultaneously. That dispatching tasks to the servers is a challenge since there has a large number of applications in the heterogeneous cloud environment servers, all application services need to cooperate with each other in the cloud computing environment network. The huge number of tasks, an appropriate and effective scheduling algorithm is to allocate these tasks to appropriate servers within the minimum completion time, and to achieve the load balancing of performance workload of the cloud system. In this paper, we present a novel improved efficient dynamic load balancing scheme to organizing the virtualized resources algorithm, called Improved Efficient Scheme (IES) algorithm in the cloud computing network. The main concept of the IES algorithm is to allocate the tasks to server host by comparing all value of makespan time of the server nodes between each task. Basically, the IES algorithm can obtain better task completion time than previous works and can achieve dynamic load balancing in cloud computing environment.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Puthal, D., Sahoo, B. P. S., Mishra, S., & Swain, S. (2015). Cloud computing features, issues, and challenges: a big picture. In International conference on computational intelligence and networks (CINE), pp. 116–123.

  2. Shawish, A., & Salama, M. (2014). Cloud computing: paradigms and technologies. In Inter-cooperative collective intelligence: Techniques and applications, Springer, Berlin Heidelberg, pp. 39–67.

    Chapter  Google Scholar 

  3. Anousha, Soheil, & Ahmadi, Mahmoud. (2013). An improved min–min task scheduling algorithm in grid computing. Lecture Notes in Computer Science Grid and Pervasive Computing,7861, 103–113.

    Article  Google Scholar 

  4. Meraji, S., & Salehnamadi, M. R. (2013). A batch mode scheduling algorithm for grid computing. Journal of Basic and Applied Scientific Research,3(4), 173–181.

    Google Scholar 

  5. Etminani, K., & Naghibzadeh, M. (2007) A min–min max–min selective algorithm for grid task scheduling, In Third IEEE/IFIP international conference in Central Asia on internet, pp. 138–144.

  6. Cheng, Dazhao, Rao, Jia, Guo, Yanfei, Jiang, Changjun, & Zhou, Xiaobo. (2016). Improving performance of heterogeneous mapreduce clusters with adaptive task tuning. IEEE Transactions on Parallel and Distributed Systems,28(3), 774–786.

    Article  Google Scholar 

  7. Braun, T. D., Siegel, H. J., Beck, N., Boloni, L. L., Reuther, A. I., Theys, M. D., Yao, B., Freund, R. F., Maheswaran, M., Robertson, J. P., & Hensgen, D. (1999). A comparison study of static mapping heuristics for a class of meta-tasks on heterogeneous computing systems. In Proceedings of the 8th heterogeneous computing workshop (HCW’1999), San Juan, Puerto Rico, USA, pp. 15–29.

  8. Thomas, A., Krishnalal, G., & Jagathy Raj, V. P. (2015). Credit based scheduling algorithm in cloud computing environment. Procedia Computer Science,46, 913–920.

    Article  Google Scholar 

  9. Gao, Xiaofeng, Kong, Linghe, Li, Weichen, Liang, Wanchao, Chen, Yuxiang, & Chen, Guihai. (2017). Traffic load balancing schemes for devolved controllers in mega data centers. IEEE Transactions on Parallel and Distributed Systems,28(2), 572–585.

    Google Scholar 

  10. Casanova, H., Legrand, A., Zagorodnov, D., & Berman, F. (2000) Heuristics for scheduling parameter sweep applications in grid environment. In Proceedings of the 9th heterogeneous computing workshop (HCW’2000), Cancun, Mexico, pp. 349–363.

  11. Dhinesh Babu, L. D., & Venkata Krishna, P. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing,13(5), 2292–2303.

    Article  Google Scholar 

  12. Maheswarana, M., Ali, S., Siegel, H. J., Hensgen, D., & Freund, R. F. (1999). Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. Journal of Parallel and Distributed Computing,59(2), 107–131.

    Article  Google Scholar 

  13. Netto, M. A., Vecchiola, C., Kirley, M., Varela, C. A., & Buyya, R. (2011). Use of run time predictions for automatic co-allocation of multicluster resources for iterative parallel applications. Journal of Parallel and Distributed Computing,71(10), 1388–1399.

    Article  Google Scholar 

  14. Mishra, M., Das, A., Kulkarni, P., & Sahoo, A. (2012). Dynamic resource management using virtual machine migrations. IEEE Communications Magazine,50(9), 34–40.

    Article  Google Scholar 

  15. Gutierrez Garcia, J. O., & Ramirez Nafarrate, A. (2015). Collaborative agents for distributed load management in cloud data centers using live migration of virtual machines. IEEE Transactions on Services Computing,8(6), 916–929.

    Article  Google Scholar 

  16. Redaa, N. M., Tawfik, A., Marzok, M. A., & Khamis, S. M. (2015). Sort-mid tasks scheduling algorithm in grid computing. Journal of Advanced Research,6(6), 987–993.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to T. K. P. Rajagopal.

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

Rajagopal, T.K.P., Venkatesan, M. & Rajivkannan, A. An Improved Efficient Dynamic Load Balancing Scheme Under Heterogeneous Networks in Hybrid Cloud Environment. Wireless Pers Commun 111, 1837–1851 (2020). https://doi.org/10.1007/s11277-019-06960-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06960-4

Keywords

Navigation