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.
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
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
Esmaeili Moshiran,A. and Babaei,S. (2020). Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm. Transactions on Machine Intelligence, 3(4), 240-250. doi: 10.47176/TMI.2020.240
MLA
Esmaeili Moshiran,A. , and Babaei,S. . "Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm", Transactions on Machine Intelligence, 3, 4, 2020, 240-250. doi: 10.47176/TMI.2020.240
HARVARD
Esmaeili Moshiran A., Babaei S. (2020). 'Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm', Transactions on Machine Intelligence, 3(4), pp. 240-250. doi: 10.47176/TMI.2020.240
CHICAGO
A. Esmaeili Moshiran and S. Babaei, "Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm," Transactions on Machine Intelligence, 3 4 (2020): 240-250, doi: 10.47176/TMI.2020.240
VANCOUVER
Esmaeili Moshiran A., Babaei S. Load Balancing of Servers in Cloud Computing Using the Lion Optimization Algorithm. Trans. Mach. Intell., 2020; 3(4): 240-250. doi: 10.47176/TMI.2020.240