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

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Cloud computing is one of the distributed resource-sharing technology that offers resources on a pay-as-you-use basis. Platform as a service, Infrastructure as a service, and Software as a Service are services provided by the Cloud. Each end user's Quality of service must be ensured by the cloud service provider. In recent days, cloud utilization is rapidly increasing. To avoid congestion and to preserve the Service Level Agreement, the large workload must be balanced across the network. In this research work, a new load balancing approach is proposed for the dynamic resource allocation process to improve stability and to increase profit. PBMM algorithm is devised for an effective load balancing process through which, resource scheduling is performed. Task size and the bidding value coded by each customer are taken into account. To optimize the waiting time, resource tables and task tables are employed. The average waiting time and response time of the special users are minimized. The simulation results show that the proposed load balancing technique ensures the maximum profit and it enhances load balancing stability by increasing the number of special users.

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

Similar content being viewed by others

References

  1. Jin, A., Song, W., & Zhuang, W. (2018). Auction-Based Resource Allocation for Sharing Cloudlets in Mobile Cloud Computing. IEEE Transactions on Emerging Topics in Computing, 6(1), 45–57. https://doi.org/10.1109/TETC.2015.2487865

    Article  Google Scholar 

  2. Li, C., & Li, L. (2017). Load-Balancing Based Cross-Layer Elastic Resource Allocation in Mobile Cloud. Wireless Personal Communications, 97, 2399–2437. https://doi.org/10.1007/s11277-017-4615-3

    Article  Google Scholar 

  3. Lin, C., Chin, H., & Deng, D. (2014). Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System. IEEE Systems Journal, 8(1), 225–234. https://doi.org/10.1109/JSYST.2013.2256320

    Article  Google Scholar 

  4. Wang, Z., Hayat, M. M., Ghani, N., & Shaban, K. B. (2017). Optimizing cloud-service performance: efficient resource provisioning via optimal workload allocation. IEEE Transactions on Parallel and Distributed Systems, 28(6), 1689–1702. https://doi.org/10.1109/TPDS.2016.2628370

    Article  Google Scholar 

  5. Balakrishna, G., & Moparthi, N. (2019). ESBL: Design and Implement A Cloud Integrated Framework for IoT Load Balancing. International Journal of Computers Communications and Control, 14(4), 459–474. https://doi.org/10.15837/ijccc.2019.4.3491

    Article  Google Scholar 

  6. Puthal, D., Obaidat, M. S., Nanda, P., Prasad, M., Mohanty, S. P., & Zomaya, A. Y. (2018). Secure and sustainable load balancing of edge data centers in fog computing. IEEE Communications Magazine, 56(5), 60–65. https://doi.org/10.1109/MCOM.2018.1700795

    Article  Google Scholar 

  7. Sthapit, S., Thompson, J., Robertson, N. M., & Hopgood, J. R. (2019). Computational load balancing on the edge in absence of cloud and fog. IEEE Transactions on Mobile Computing, 18(7), 1499–1512. https://doi.org/10.1109/TMC.2018.2863301

    Article  Google Scholar 

  8. Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., & Wang, X. (2017). Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Transactions on Smart Grid, 8(4), 1943–1955. https://doi.org/10.1109/TSG.2015.2512712

    Article  Google Scholar 

  9. Mao, Y., Chen, X., & Li, X. (2014). Max-min task scheduling algorithm for load balance in cloud computing. Proceedings of International Conference on Computer Science and Information Technology. https://doi.org/10.1007/978-81-322-1759-6_53

    Article  Google Scholar 

  10. Kim, K. H., Beloglazov, A., & Buyya, R. (2009). Power-aware provisioning of Cloud resources for real-time services. Proceedings of the 7th International Workshop on Middleware for Grids, Clouds and e-Science - MGC ’09. doi:https://doi.org/10.1145/1657120.1657121

  11. Papagianni, C., Leivadeas, A., Papavassiliou, S., Maglaris, V., Cervelló-Pastor, C., & Monje, Á. (2013). On the optimal allocation of virtual resources in cloud computing networks. IEEE Transactions on Computers, 62(6), 1060–1071. https://doi.org/10.1109/TC.2013.31

    Article  MathSciNet  MATH  Google Scholar 

  12. Xiao, Z., Song, W., & Chen, Q. (2013). Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1107–1117. https://doi.org/10.1109/TPDS.2012.283

    Article  Google Scholar 

  13. J. Tai, J. Zhang, J. Li, W. Meleis and N. Mi, (2011). ArA: Adaptive resource allocation for cloud computing environments under bursty workloads. 30th IEEE International Performance Computing and Communications Conference, doi: https://doi.org/10.1109/PCCC.2011.6108060.

  14. T. Tomita and S. Kuribayashi, (2011) Congestion control method with fair resource allocation for cloud computing environments. Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2011, pp. 1–6, doi: https://doi.org/10.1109/PACRIM.2011.6032858.

  15. Al-Rahayfeh, Amer, Atiewi, Saleh, Abuhussein, Abdullah, & Almiani, Muder. (2019). Novel approach to task scheduling and load balancing using the dominant sequence clustering and mean shift clustering algorithms. Future Internet. https://doi.org/10.3390/fi11050109

    Article  Google Scholar 

  16. Pradeep, K., & Prem Jacob, T. (2018). A hybrid approach for task scheduling using the cuckoo and harmony search in cloud computing environment. Wireless Personal Communications, 101, 2287–2311. https://doi.org/10.1007/s11277-018-5816-0

    Article  Google Scholar 

  17. Chitra Devi, D., & RhymendUthariaraj, V. (2016). Load balancing in cloud computing environment using improved weighted round robin algorithm for nonpreemptive dependent tasks. The Scientific World Journal. https://doi.org/10.1155/2016/3896065

    Article  Google Scholar 

  18. Xiaolong, Xu., Shucun, Fu., Cai, Qing, Tian, Wei, Liu, Wenjie, Dou, Wanchun, Sun, Xingming, & Liu, Alex X. (2018). Dynamic resource allocation for load balancing in fog environment. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2018/6421607

    Article  Google Scholar 

  19. Huy, D. T. P., Rodriguez, J., Gameiro, A., et al. (2007). Dynamic resource allocation for beyond 3G cellular networks. Wireless Personal Communications, 43, 1727–1740. https://doi.org/10.1007/s11277-007-9339-3

    Article  Google Scholar 

  20. C. H. Benet, K. A. Noghani, A. Kassler, O. Dobrijevic and P. Jestin. (2017) Policy-based routing and load balancing for EVPN-based data center interconnections. 2017 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), https://doi.org/10.1109/NFV-SDN.2017.8169841.

  21. Rajagopal, T. K. P., Venkatesan, M., & Rajivkannan, A. (2020). An improved efficient dynamic load balancing scheme under heterogeneous networks in hybrid cloud environment. Wireless Personal Communications, 111, 1837–1851. https://doi.org/10.1007/s11277-019-06960-4

    Article  Google Scholar 

  22. Kim, H. Y., & Kim, J. (2017). An energy-efficient balancing scheme in wireless sensor networks. Wireless Personal Communications, 94, 17–29. https://doi.org/10.1007/s11277-015-3154-z

    Article  Google Scholar 

  23. Ram Krishana, , Dr. Vijay Laxmi “IEEE 802.11 WLAN Load Balancing for Network Performance Enhancement” 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015), Ghaziabad, India.

  24. Chinnaiah, V., GudiPudi, S., Somasundaram, T., et al. (2018). A cloud resource allocation strategy based on fitness based live migration and clustering. Wireless Pers Commun, 98, 2943–2958. https://doi.org/10.1007/s11277-017-5009-2

    Article  Google Scholar 

  25. AskarizadeHaghighi, M., Maeen, M., & Haghparast, M. (2019). An energy-efficient dynamic resource management approach based on clustering and meta-heuristic algorithms in cloud computing iaas platforms. Wireless Pers Commun, 104, 1367–1391. https://doi.org/10.1007/s11277-018-6089-3

    Article  Google Scholar 

  26. Ataee, M., & Mohammadi, A. (2017). Energy-efficient resource allocation for adaptive modulated mimo–ofdm heterogeneous cloud radio access networks. Wireless Personal Communications, 95, 4847–4866. https://doi.org/10.1007/s11277-017-4127-1

    Article  Google Scholar 

  27. Praveenchandar, J., & Tamilarasi, A. (2021). Dynamic resource allocation with optimized task scheduling and improved power management in cloud computing. J Ambient Intell Human Comput, 12, 4147–4159. https://doi.org/10.1007/s12652-020-01794-6

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Praveenchandar.

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

Praveenchandar, J., Tamilarasi, A. An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments. Wireless Pers Commun 122, 3757–3776 (2022). https://doi.org/10.1007/s11277-021-09110-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-021-09110-x

Keywords

Navigation