Volume 2 (2), December 2019, Pages 178-182

Ulphat Bakhishov

Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.


Exascale systems are the concept of HPC systems that able to perform one exaflop (1018 floating-point operations) per second in a dynamic and interactive environment. As the traditional HPC systems, the major challenge of this system is load balancing. Providing load balancing in dynamic and interactive nature requires a model which handles dynamic and interactive events and allows to manage load distribution over the system. It strongly depends on the distribution degree of the system. This paper defined a new model of load flow while imbalance occurred in the node of a fully distributed Exascale system.


Distributed Exascale Computing system, Load Balancing, Dynamic and Interactive Nature, Load distribution model




Bakhishoff, U. (2018). Applying Multiple Multidimensional Knapsack Problem to Dynamic Load Balancing in Distributed Exascale computing environment. Azerbaijan Journal of High Performance Computing, 1(2), 214-218.

Balasangameshwara, J., & Raju, N. (2013). Performance-Driven Load Balancing with a Primary-Backup Approach for Computational Grids with Low Communication Cost and Replication Cost. Ieee Transactions on Computers, 62(5), 990-1003. doi:10.1109/tc.2012.44

Dharmik, R. C., & Sathe, S. R. (2018). A Sender Initiated Dynamic and Decentralized Load Balancing algorithm for Computational Grid Environment Using Variable CPU Usage. International Journal of Applied Engineering Research, 13(1), 189-194.

Domanal, S. G., & Reddy, G. R. M. (2015, November). Load balancing in cloud environment using a novel hybrid scheduling algorithm. In 2015 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM) (pp. 37-42). IEEE.

Dongarra, J., Beckman, P., Moore, T., Aerts, P., Aloisio, G., Andre, J. C., Yelick, K. (2011). The International Exascale Software Project roadmap. International Journal of High Performance Computing Applications, 25(1), 3-60. doi:10.1177/1094342010391989

Eicker, N. The DEEP Project.

Fairuzullah, A., Noraziah, A., Arshah, R. A., & Herawan, T. (2019). Optimize Performance Load Balancing Techniques Using Binary Vote Assignment Grid Quorum (BVAGQ): A Systematic Review. In Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) (pp. 31-39). Springer, Singapore.

Khaneghah, E. M., Mollasalehi, F., Aliev, A. R., Ismayilova, N., & Bakhishoff, U. (2018). Challenges of Load Balancing to Support Distributed Exascale Computing Environment. In Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA) (pp. 100-106).The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).

Khaneghah, E. M., & Sharifi, M. (2014). AMRC: an algebraic model for reconfiguration of high performance cluster computing systems at runtime. Journal of Supercomputing, 67(1), 1-30. doi:10.1007/s11227-013-0982-z

Kumar, P., & Kumar, R. (2019). Issues and Challenges of Load Balancing Techniques in Cloud Computing: A Survey. Acm Computing Surveys, 51(6), 35. doi:10.1145/3281010

Lieber, M., Goebner, K., & Nagel, W. E. (2016, September). The potential of diffusive load balancing at large scale. In Proceedings of the 23rd European MPI Users’ Group Meeting (pp. 154-157). ACM.

Mirtaheri, S. L., & Grandinetti, L. (2017). Dynamic load balancing in distributed exascale computing systems. Cluster Computing-the Journal of Networks Software Tools and Applications, 20(4), 3677-3689. doi:10.1007/s10586-017-0902-8

Rathore, N., & Chana, I. (2013, September). A sender initiate based hierarchical load balancing technique for grid using variable threshold value. In 2013 IEEE International Conference on Signal Processing, Computing and Control (ISPCC) (pp. 1-6). IEEE.

Sharifi, M., Mirtaheri, S. L., & Khaneghah, E. M. (2010). A dynamic framework for integrated management of all types of resources in P2P systems. Journal of Supercomputing, 52(2), 149-170. doi:10.1007/s11227-009-0281-x

Sharma, S., Singh, S., & Sharma, M. (2008). Performance analysis of load balancing algorithms. World Academy of Science, Engineering and Technology, 38(3), 269-272.

Wang, K., Brandstatter, K., & Raicu, I. (2013). SimMatrix: SIMulator for MAny-Task computing execution fabRIc at eXascale. High Performance Computing Symposium 2013 (Hpc 2013) - 2013 Spring Simulation Multi-Conference (Springsim’13), 45(6), 66-74.