Volume 3 (2), December 2020, Pages 190-195

Nigar T. Ismayilova

High Performance Computing Research Advance Center, Department of General and Applied Mathematics, Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.


This paper examines the role of applying different artificial intelligence techniques for the implementation of load balancing in the dynamic environment of distributed multi-core computing systems. Were investigated several methods to optimize the assignment process between computing nodes and executing tasks after the occurrence of a dynamic and interactive event, when traditional discrete load balancing techniques are ineffective.


Exascale Computing, AI, Load Balancer, Graph Matching, Hybrid techniques.





Alakeel, A. M. (2010). A guide to dynamic load balancing in distributed computer systems. International Journal of Computer Science and Information Security, 10(6), 153-160.

Atayero, A. A., & Luka, M. K. (2012). Adaptive neuro-fuzzy inference system for dynamic load balancing in 3GPP LTE. International Journal of Advanced Research in Artificial Intelligence, 1(1), 11-16.

Bakhishoff, U., Khaneghah, E. M., Aliev, A. R., & Showkatabadi, A. R. (2020). DTHMM ExaLB: discrete-time hidden Markov model for load balancing in distributed exascale computing environment. Cogent Engineering, 7(1), 1743404.

Catalyurek, U. V., Boman, E. G., Devine, K. D., Bozdağ, D., Heaphy, R. T., & Riesen, L. A. (2009). A repartitioning hypergraph model for dynamic load balancing. Journal of Parallel and Distributed Computing, 69(8), 711-724.

Di Nitto, E., Dubois, D. J., Mirandola, R., Saffre, F., & Tateson, R. (2008, November). Applying self-aggregation to load balancing: experimental results. In Proceedings of the 3rd International Conference on Bio-Inspired Models of Network, Information and Computing Sytems (pp. 1-8).

Godfrey, B., Lakshminarayanan, K., Surana, S., Karp, R., & Stoica, I. (2004, March). Load balancing in dynamic structured P2P systems. In IEEE INFOCOM 2004 (Vol. 4, pp. 2253-2262). IEEE.

Kuila, P., & Jana, P. K. (2012). Energy efficient load-balanced clustering algorithm for wireless sensor networks. Procedia Technology, 6, 771-777.

Kwok, Y. K., & Cheung, L. S. (2004). A new fuzzy-decision based load balancing system for distributed object computing. Journal of Parallel and Distributed Computing, 64(2), 238-253.

Lee, S. P., & Nahm, E. S. (2012, August). Development of an optimal load balancing algorithm based on ANFIS modeling for the clustering web-server. In International Conference on Hybrid Information Technology (pp. 783-790). Springer, Berlin, Heidelberg.

Li, J., Luo, G., Cheng, N., Yuan, Q., Wu, Z., Gao, S., & Liu, Z. (2018). An end-to-end load balancer based on deep learning for vehicular network traffic control. IEEE Internet of Things Journal, 6(1), 953-966.

Li, K., Xu, G., Zhao, G., Dong, Y., & Wang, D. (2011, August). Cloud task scheduling based on load balancing ant colony optimization. In 2011 Sixth Annual China Grid Conference (pp. 3-9). IEEE.

Lin, C. C., Chin, H. H., & Deng, D. J. (2013). Dynamic multiservice load balancing in cloud-based multimedia system. IEEE Systems Journal, 8(1), 225-234.

Muñoz, P., Barco, R., & de la Bandera, I. (2013). Optimization of load balancing using fuzzy Q-learning for next generation wireless networks. Expert Systems with Applications, 40(4), 984-994.

Naaz, S., Alam, A., & Biswas, R. (2011). Effect of different defuzzification methods in a fuzzy based load balancing application. International Journal of Computer Science Issues (IJCSI), 8(5), 261.

Otten, L., & Dechter, R. (2011). Finding most likely haplotypes in general pedigrees through parallel search with dynamic load balancing. In Biocomputing 2011 (pp. 26-37).

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.

Sethi, S., Sahu, A., & Jena, S. K. (2012). Efficient load balancing in cloud computing using fuzzy logic. IOSR Journal of Engineering, 2(7), 65-71.

Shaout, A., & McAuliffe, P. (1998). Job scheduling using fuzzy load balancing in distributed system. Electronics Letters, 34(20), 1983-1985.

Sharma, D., & Aggarwal, V. B. (2015). An effective mechanism for improving performance of load balancing system in cluster computing. International Journal of Computer Applications, 115 (7), 21-27.

Sigal, L., & Glauberman, A. (2012). U.S. Patent No. 8,185,909. Washington, DC: U.S. Patent and Trademark Office.

Suresh, M., & Karthik, S. (2014, March). A load balancing model in public cloud using ANFIS and GSO. In 2014 International Conference on Intelligent Computing Applications (pp. 85-89). IEEE.

Visalakshi, P., & Sivanandam, S. N. (2009). Dynamic task scheduling with load balancing using hybrid particle swarm optimization. Int. J. Open Problems Compt. Math, 2(3), 475-488.

Wang, Y. C., Peng, W. C., Chang, M. H., & Tseng, Y. C. (2007, August). Exploring load-balance to dispatch mobile sensors in wireless sensor networks. In 2007 16th International Conference on Computer Communications and Networks (pp. 669-674). IEEE.

Zhao, J., Yang, K., Wei, X., Ding, Y., Hu, L., & Xu, G. (2015). A heuristic clustering-based task deployment approach for load balancing using Bayes theorem in cloud environment. IEEE Transactions on Parallel and Distributed Systems, 27(2), 305-316.