Heuristic Greedy-Gradient Route Search Method for Finding an Optimal Traffic Distribution in Telecommunication Networks

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Algorithms Pub Date : 2023-12-23 DOI:10.3390/a17010007
Konstantin Gaipov, Daniil Tausnev, Sergey Khodenkov, N. Shepeta, Dmitry Malyshev, Aleksey Popov, L. Kazakovtsev
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引用次数: 0

Abstract

Rapid growth in the volume of transmitted information has lead to the emergence of new wireless networking technologies with variable heterogeneous topologies. With limited radio frequency resources, optimal routing problems arise, both at the network design stage and during its operation. We propose an algorithm based on a minimum loss intensity (greedy-gradient algorithm) to search for optimal routes of information transmission in telecommunication networks. The relevance of the developed algorithm is determined by its practical use in data-transmitting modeling systems. The proposed algorithm satisfies several requirements, such as the speed of the calculations performed, the fulfillment of the conditions for its convergence, and its independence on the selected loss probability function, as well as on the network topology. The idea of the algorithm is a step-by-step recalculation of metrics based on derivatives of the loss intensity function with simultaneous redistribution of information flows along the routes determined by the Floyd algorithm. The comparative efficiency of the proposed algorithm is demonstrated by computational experiments on various network topologies (up to 100 nodes) with various traffic intensities.
在电信网络中寻找最佳流量分布的启发式贪婪梯度路由搜索法
传输信息量的快速增长导致了具有可变异构拓扑结构的新型无线网络技术的出现。由于射频资源有限,在网络设计阶段和运行过程中都会出现优化路由问题。我们提出了一种基于最小损耗强度的算法(贪婪梯度算法),用于搜索电信网络中的最佳信息传输路径。所开发算法的相关性取决于其在数据传输建模系统中的实际应用。所提出的算法满足多项要求,如计算速度快、满足收敛条件、与所选损失概率函数和网络拓扑无关。该算法的思路是根据损失强度函数的导数逐步重新计算指标,同时沿 Floyd 算法确定的路由重新分配信息流。通过对各种网络拓扑结构(最多 100 个节点)和各种流量强度的计算实验,证明了所提算法的比较效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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