Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/15881
Title: Analytical review of the methods of dynamic load balancing under conditions of uncertainty in the execution time of tasks
Authors: Shiriaev, E. M.
Ширяев, Е. М.
Kucherov, N. N.
Кучеров, Н. Н.
Kuchukov, V. A.
Кучуков, В. А.
Keywords: Computational GCDs;Neural networks;Load balancing;Evolutionary algorithms;Distributed computing;Neural networks
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Shiriaev E.M., Kycherov N.N., Kuchukov V.A. Analytical review of the methods of dynamic load balancing under conditions of uncertainty in the execution time of tasks // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - Pages 674 - 677. - Номер статьи 9396502
Series/Report no.: Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
Abstract: Сomputationally complex problems can be solved using distributed computing resources, but the problem arises in the optimal way of distributing tasks between computing nodes in order to reduce the total execution time. However, the computation time for a task on the same device is not constant and can change dynamically over time. In this article, we explore methods of dynamic load balancing in order to minimize the computation time of the problem. We study three groups of methods based on the use of probability theory and mathematical statistics methods, evolutionary algorithms and artificial neural networks. We have shown that methods based on artificial neural networks can reduce the computation time of the problem and minimize the complexity of the dynamic load balancing algorithm
URI: http://hdl.handle.net/20.500.12258/15881
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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