Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing

##plugins.themes.academic_pro.article.sidebar##

Published Nov 26, 2022
https://doi.org/10.47164/ijngc.v13i5.930
Download
Requires Subscription PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 13, Special Issue 5, November 2022

##plugins.themes.academic_pro.article.main##

Prof.Amol D Gaikwad
RTMNU Nagpur University YCCE
Dr.Kavita R. Singh
RTMNU Nagpur University

Abstract

You can’t obtain the outcomes you need without planning, thus it’s at the heart of cloud computing. This article’s major goal is to decrease value-added time, increase resource utilisation, and make cloud services viable for a single activity. In recent years, metaheuristic algorithms have drew attention to the correct functioning of work scheduling algorithms among the many job scheduling techniques. With sports leagues, the algorithm based on the League Championship (LCA) is fascinating because it can be used to identify the best team/task for programming.This article uses the Improved League Championship Algorithm (ILCA) to schedule tasks, reducing deployment time, cloud usage, and cost. The ILCA is implemented through the Cloudsim simulator and the Java programming language with a nonpreventive planning strategy. ILCA also enhances economies of scale and minimises the value of using the cloud. As it has proven to be versatile in terms of time to manufacture, resource usage and economics, ILCA could be a good candidate for a cloud broker as it has proven to be versatile in terms
of time to manufacture, resource usage and economics usage.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Gaikwad, A. ., & Singh, K. . (2022). Improved League Championship Algorithm (ILCA) for Load Balancing in Cloud Computing . International Journal of Next-Generation Computing, 13(5). https://doi.org/10.47164/ijngc.v13i5.930
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Jena, T., Mohanty, J.R. (2017). GA-based customer conscious resource allocation and task scheduling in multi-cloud computing. Arab J Sci. Eng., 43(8): 4115- 4130. https://doi.org/10.1007/s13369-017-2766-x DOI: https://doi.org/10.1007/s13369-017-2766-x
  2. Kalra, M., Singh, S. (2015). A review of metaheuristic scheduling techniques in cloud computing. Egyptian Informatics Journal, 16(3): 275-295. https://doi.org/10.1016/j.eij.2015.07.001 DOI: https://doi.org/10.1016/j.eij.2015.07.001
  3. Dubey, K., Kumar, M., Sharma, S.C. (2017) Modified HEFT algorithm for task scheduling in cloud computing. In Science Direct, Procedia Computer Science, 125: 725- 732. https://doi.org/10.1016/j.procs.2017.12.093 DOI: https://doi.org/10.1016/j.procs.2017.12.093
  4. Kashan, A.H. (2009) League championship algorithm: A new algorithm for numerical function optimization. 2009 International Conference of Soft Computing and Pattern Recognition, Published in IEEE Computer Society, pp. 43-48. https://doi.org/10.1109/SoCPaR.2009.21 DOI: https://doi.org/10.1109/SoCPaR.2009.21
  5. Abdulhamid, S.M., Latiff, M.S.A., Abdul-Salaam, G., Madni, S.H.H. (2016). Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm. PLoS ONE, 11(7): e0158102. https://doi.org/10.1371/journal.pone.0158102 DOI: https://doi.org/10.1371/journal.pone.0158102
  6. Ge, Y., Wei, G. (2010). GA-based task scheduler for the cloud computing systems. In: International Conference web Information System Mining, WISM 2010. pp. 181– 186. https://doi.org/10.1109/WISM.2010.87 DOI: https://doi.org/10.1109/WISM.2010.87
  7. Kaur, S., Verma, A. (2012) An efficient approach to genetic algorithm for task scheduling in cloud computing environment. I.J. Information Technology and Computer Science, 10: 74-79. https://doi.org/10.5815/ijitcs.2012.10.09 DOI: https://doi.org/10.5815/ijitcs.2012.10.09
  8. Liu, A., Wang, Z. (2008). Grid task scheduling based on adaptive ant colony algorithm. In: International conference on Management E-commerce E-government Grid, pp 415–418. https://doi.org/10.1109/ICMECG.2008.50 358 DOI: https://doi.org/10.1109/ICMECG.2008.50
  9. Zhang, Z., Zhang, X. (2010). A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In: 2nd International Conference on Industrial Mechatronics and Automation, pp 240–243. https://doi.org/10.1109/icindma.2010.5538385 DOI: https://doi.org/10.1109/ICINDMA.2010.5538385
  10. Gupta A, Kapoor L, Wattal M. C2c (cloud-to-cloud): An ecosystem of cloud service providers for dynamic resource provisioning. InInternational Conference on Advances in Computing and Communications 2011 Jul 22 (pp. 501-510). Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-22709-7_49
  11. Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia, Australia. http://dx.doi.org/10.1109/ICNN.1995.488968 DOI: https://doi.org/10.1109/ICNN.1995.488968
  12. Ramezani, F., Lu, J., Hussain, F.K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. Int J Parallel Program, 42:739–754. https://doi.org/10.1007/s10766-013-0275-4 DOI: https://doi.org/10.1007/s10766-013-0275-4
  13. Pooranian, Z., Shojafar, M., Abawajy, J.H., Abraham, A. (2015) An efficient meta-heuristic algorithm for grid computing. J Comb Optim (2015), New York. https://doi.org/10.1007/s10878-013-9644-6 DOI: https://doi.org/10.1007/s10878-013-9644-6
  14. Ga̧sior, J., Seredyński, F. (2013). Multi-objective parallel machines scheduling for fault-tolerant cloud systems. Algorithms and Architectures for Parallel Processing: Springer. pp. 247–256. https://doi.org/10.1007/978-3- 319-03859-9_21 DOI: https://doi.org/10.1007/978-3-319-03859-9_21
  15. Chen, Z.G., Du, K.J., Zhan, Z.H., Zhang, J. (2015). Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm. Evolutionary Computation (CEC), 2015 IEEE Congress on; 2015: IEEE. https://doi.org/10.1109/CEC.2015.7256960 DOI: https://doi.org/10.1109/CEC.2015.7256960
  16. Liu, X.F., Zhan, Z.H., Du, K.J., Chen, W.N. (2014). Energy aware virtual machine placement scheduling in cloud computing based on ant colony optimization approach. Proceedings of the 2014 Conference on Genetic and Evolutionary Computation; 2014: ACM. https://doi.org/10.1145/2576768.2598265 DOI: https://doi.org/10.1145/2576768.2598265
  17. The NASA Ames iPSC/860 log by CS Huji labs parallelworkload.