Efficient Load Balancing of Resources for Different Cloud Service Providers in Cloud Computing

Authors

  • Upanshu Kumar  Department of Information Technology, NRI Institute of Information Science and Technology, Bhopal, India
  • Shatendra Dubey  Department of Information Technology, NRI Institute of Information Science and Technology, Bhopal, India

DOI:

https://doi.org/10.32628/CSEIT239016

Keywords:

Cloud Computing, Load Balancing, Task Scheduling, Round Robin

Abstract

In distributed computing cloud computing is an emerging technology which provides pay per model as per user demand or requirement. Cloud has a collection of virtual machines which facilities both computational and storage requirement. Scheduling and Load balancing are the main challenges in the cloud computing on which we are emphasizing. Scheduling is the process to control the order of work going to be performed by computer system. Load balancing has an important role in the performance in cloud computing. Better load balancing will make cloud computing more efficient and will also increase user satisfaction. It provides a way to handle several inquiries residing inside cloud computing environment set. Complete balancing acquires two tasks, one is resource provisioning/resource allocation and task scheduling throughout the system. In the proposed research paper, we are presenting a hybrid algorithm created by FCFS and Round Robin algorithms. As the Round Robin is the easiest algorithm that's why it is frequently used and the first preference for implementing easy schedulers. The Round Robin algorithm only requires a list of nodes. In the proposed solution we have eliminated the drawbacks of simple Round Robin algorithm by introducing assignment of time slices to different processes depending upon priorities.

References

  1. Rajat Saxena and Somnath Dey, Cloud Shield: Effective Solution for DDoS in Cloud, In Internet and Distributed Computing Systems, 8th International Conference, IDCS 2015, Windsor, UK, 2015. Proceedings, September 2-4, pp. 3-10, (2015).
  2. Rajat Saxena and Somnath Dey, \DDoS Prevention using Third Party Auditor in Cloud Computing", Iran Journal of Computer Science, Vol. 2, No. 4, pp. 231-244, 2019.
  3. L. M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, \A break in the clouds: Towards a cloud definition," SIGCOMM Computation. Communication. Rev., vol. 39, no. 1, pp. 50{55, Dec 2008.
  4. R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic,  Cloud computing and emerging it platforms: Vision, hype, and reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, no. 6, pp. 599--616, 2009.
  5. R. Buyya, R. Ranjan, and R. N. Calheiros, “Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services," in International Conference on Algorithms and Architectures for Parallel Processing. Busan, Korea, May, 2010, pp. 13-31.
  6. T. Kurze, M. Klems, D. Bermbach, A. Lenk, S. Tai, and M. Kunze, “Cloud federation," in The Second International Conference on Cloud Computing, GRIDs, and Virtualization, Rome, Italy, Sept. 2011, pp. 32--38.
  7. “Gartner - cloud services brokerage," 2013.[Online]. Available:http://www.gartner.com/it-glossary/cloud-services-brokerage-csb
  8. M. Aazam and E.-N. Huh, \Broker as a service (baas) pricing and resource estimation model," in IEEE 6th International Conference on Cloud Computing Technology and Science (CloudCom), Singapore, Dec., 2014, pp. 463--468.
  9. E. Triantaphyllou, B. Shu, S. N. Sanchez, and T. Ray, \Multi-criteria decision making: an operations research approach," Encyclopedia of electrical and electronics engineering, vol. 15, no. 1998, pp. 175--186, 1998.
  10. J. Lu, G. Zhang, D. Ruan, and F. Wu, Multi-objective group decision making: methods, software and applications with fuzzy set techniques. World Scientific, 2007.
  11. R. Fullr and P. Majlender, \An analytic approach for obtaining maximal entropy owa operator weights," Fuzzy Sets and Systems, vol. 124, no. 1, pp. 53 --57, 2001.
  12. S. Opricovic and G.-H. Tzeng, \Compromise solution by mcdm methods: A comparative analysis of vikor and topsis," European Journal of Operational Research, vol. 156, no. 2, pp. 445 -- 455, 2004.
  13. F. Aznoli and N. J. Navimipour, “Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions," Journal of Network and Computer Applications, vol. 77, no. Supplement C, pp. 73 -- 86, 2017.
  14. H. Ma, H. Zhu, Z. Hu, W. Tang, and P. Dong, “Multi-valued collaborative qos prediction for cloud service via time series analysis," Future Generation Computer Systems, vol. 68, no. Supplement C, pp. 275 --288, 2017.

Downloads

Published

2023-02-28

Issue

Section

Research Articles

How to Cite

[1]
Upanshu Kumar, Shatendra Dubey, " Efficient Load Balancing of Resources for Different Cloud Service Providers in Cloud Computing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 1, pp.09-16, January-February-2023. Available at doi : https://doi.org/10.32628/CSEIT239016