Transactions on Machine Intelligence

Transactions on Machine Intelligence

Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm

Document Type : Original Article

Authors
1 Department of Computer Engineering, Faculty of Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Abstract
The growing demand for rapid processing of large and heavy computational tasks, coupled with the advancements in networks and distributed systems, has led to the development of cloud computing systems. In cloud computing, all user needs are provided in a shared environment as services on-demand and as required. One of the significant challenges in cloud-based systems infrastructure is solving the problem of optimal and efficient load distribution. In other words, assigning more virtual machines to a single physical server leads to issues such as non-optimal resource allocation and load imbalance. Hence, the load among physical servers and, consequently, the entire system load must be balanced. It has been proven that the load balancing problem in cloud computing falls under the NP-hard category, and thus, various researchers have used heuristic, metaheuristic, greedy, and other algorithms to solve it. In this paper, the load balancing problem in cloud computing is considered as an optimization problem, and the Lion Optimization Algorithm (LOA) is used to solve it. The proposed method is simulated in MATLAB software and compared with Genetic Algorithm (GA) and Simulated Annealing (SA). The experimental results indicate that the proposed approach significantly improves load balancing in the allocation of virtual machines compared to GA and SA algorithms.
Keywords

  • Neupane, D., & Seok, J.-H. (2020). Bearing fault detection and diagnosis using Case Western Reserve University dataset with deep learning approaches: A review. IEEE Access, 8, 93155-93178. https://doi.org/10.1109/ACCESS.2020.2990528
  • Ghorbanian, S. A., Ahmadi, A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M. I. S., Hamed, S., Moghaddam, A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., Brisco, B., Mirmazloumi, S. M., & Ghahremanloo, M. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326-5350. https://doi.org/10.1109/JSTARS.2020.3021052
  • Berg, G., Rybakova, D., Fischer, D., Cernava, T., Vergès, M. C., Charles, T. C., Chen, X., Cocolin, L., Eversole, K., Corral, G., Kazou, M., Kinkel, L., Lange, L., Lima, N., Loy, A., Macklin, J., Maguin, E., Mauchline, T., McClure, R., Mitter, B., Ryan, M., Sarand, I., Smidt, H., Schelkle, B., Roume, H., Kiran, G. S., Selvin, J., de Souza, R. S. C., Overbeek, L. V., Singh, B., Wagner, M., Walsh, A. M., Sessitsch, A., & Schloter, M. (2020). Microbiome definition re-visited: Old concepts and new challenges. Microbiome, 8. https://doi.org/10.1186/s40168-020-00875-0
  • Boehler, C., Carli, S., Fadiga, L., Stieglitz, T., & Asplund, M. (2020). Tutorial: Guidelines for standardized performance tests for electrodes intended for neural interfaces and bioelectronics. Nature Protocols, 1-22. https://doi.org/10.1038/s41596-020-0389-2
  • Cao, K., Liu, Y., Meng, G., & Sun, Q. (2020). An overview on edge computing research. IEEE Access, 8, 85714-85728. https://doi.org/10.1109/ACCESS.2020.2991734
  • Zimmermann, A., Wunderlich, J., Müller, L., Buchner, G., Marxen, A., Michailos, S., Armstrong, K., Naims, H., McCord, S., Styring, P., Sick, V., & Schomäcker, R. (2020). Techno-economic assessment guidelines for CO2 utilization. Frontiers in Energy Research, 8. https://doi.org/10.3389/fenrg.2020.00005
  • Van Meerbeek, B., Yoshihara, K., Van Landuyt, K. L., Yoshida, Y., & Peumans, M. (2020). From Buonocore's pioneering acid-etch technique to self-adhering restoratives. A status perspective of rapidly advancing dental adhesive technology. The Journal of Adhesive Dentistry, 22(1), 7-34. https://doi.org/10.3290/j.jad.a43994
  • Kurdi, B., Alshurideh, M., Salloum, S., Obeidat, Z., & Al-Dweeri, R. (2020). An empirical investigation into examination of factors influencing university students' behavior towards e-learning acceptance using SEM approach. International Journal of Interactive Mobile Technologies, 14, 19-41. https://doi.org/10.3991/ijim.v14i02.11115
  • Ayaz, M., Pasha, M. F., Alzahrani, M., Budiarto, R., & Stiawan, D. (2020). The Fast Health Interoperability Resources (FHIR) standard: Systematic literature review of implementations, applications, challenges and opportunities. JMIR Medical Informatics, 9. https://doi.org/10.2196/21929
  • Wang, Z., Du, Y., Wei, K., Han, K., Xu, X., Wei, G., Tong, W., Zhu, P., Ma, J., Wang, J., Wang, G., Yan, X., Xiang, J., Huang, H., Li, R., Wang, X., Wang, Y., Sun, S., Suo, S., Gao, Q., & Su, X. (2022). Vision, application scenarios, and key technology trends for 6G mobile communications. Science China Information Sciences, 65. https://doi.org/10.1007/s11432-021-3351-5
  • Velte, T., Velte, A., & Elsenpeter, R. (2009). Cloud computing: A practical approach. McGraw-Hill, Inc.
  • Liu, Z., & Wang, X. (2012). A PSO-based algorithm for load balancing in virtual machines of cloud computing environment. In International Conference in Swarm Intelligence (pp. 142-147). https://doi.org/10.1007/978-3-642-30976-2_17
  • Acharya, J., Mehta, M., & Saini, B. (2016). Particle swarm optimization based load balancing in cloud computing. In 2016 International Conference on Communication and Electronics Systems (ICCES) (pp. 1-4). https://doi.org/10.1109/CESYS.2016.7889943
  • Al Nuaimi, K., Mohamed, N., Al Nuaimi, M., & Al-Jaroodi, J. (2012). A survey of load balancing in cloud computing: Challenges and algorithms. In Network Cloud Computing and Applications (NCCA), 2012 Second Symposium on (pp. 137-142). https://doi.org/10.1109/NCCA.2012.29
  • Nishant, K., Sharma, P., Gupta, V., Singh, K., Nitin, & Rastogi, R. (2012). Load Balancing of Nodes in Cloud Using Ant Colony Optimization. In 2012 UKSim 14th International Conference on Computer Modelling and Simulation (pp. 3-8). https://doi.org/10.1109/UKSim.2012.11
  • Zhang, Z., & Zhang, X. (2010). A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation. In Industrial Mechatronics and Automation (ICIMA), 2010 2nd International Conference on (Vol. 2, pp. 240-243). https://doi.org/10.1109/ICINDMA.2010.5538385
  • Makasarwala, H. A., & Hazari, P. (2016). Using genetic algorithm for load balancing in cloud computing. In 2016 8th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-6). https://doi.org/10.1109/ECAI.2016.7861166
  • D. B., & Krishna, P. V. (2013). Honey bee behavior inspired load balancing of tasks in cloud computing environments. Applied Soft Computing, 13(5), 2292-2303. https://doi.org/10.1016/j.asoc.2013.01.025
  • Yazdani, M., & Jolai, F. (2016). Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering, 3(1), 24-36. https://doi.org/10.1016/j.jcde.2015.06.003
  • Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-based system load balancing in cloud computing using particle swarm optimization. International Journal of Parallel Programming, 42(5), 739-754. https://doi.org/10.1007/s10766-013-0275-4
Volume 3, Issue 4
Autumn 2020
Pages 240-250

  • Receive Date 29 June 2020
  • Revise Date 16 September 2020
  • Accept Date 22 December 2020