College of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China.
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Phys Rev E. 2019 Jul;100(1-1):012310. doi: 10.1103/PhysRevE.100.012310.
An efficient flow assignment strategy is of great importance to alleviate traffic congestion on multilayer networks. In this work, by considering the roles of nodes' local structures on the microlevel, and the different transporting speeds of layers in the macrolevel, an effective traffic-flow assignment strategy on multilayer networks is proposed. Both numerical and semianalytical results indicate that our proposed flow assignment strategy can reasonably redistribute the traffic flow of the low-speed layer to the high-speed layer. In particular, preferentially transporting the packets through small-degree nodes on the high-speed layer can enhance the traffic capacity of multilayer networks. We also find that the traffic capacity of multilayer networks can be improved by increasing the network size and the average degree of the high-speed layer. For a given multilayer network, there is a combination of optimal macrolevel parameter and optimal microlevel parameter with which the traffic capacity can be maximized. It is verified that real-world network topology does not invalidate the results. The semianalytical predictions agree with the numerical simulations.
一种有效的流量分配策略对于缓解多层网络中的交通拥堵具有重要意义。在这项工作中,通过考虑微观层面上节点局部结构的作用,以及宏观层面上各层不同的传输速度,我们提出了一种有效的多层网络流量分配策略。数值和半解析结果都表明,我们提出的流量分配策略可以合理地将低速层的流量重新分配到高速层。特别是,通过优先在高速层上通过小度数节点传输数据包,可以提高多层网络的交通容量。我们还发现,通过增加网络规模和高速层的平均度数,可以提高多层网络的交通容量。对于给定的多层网络,存在一组最优的宏观参数和最优的微观参数组合,可以实现最大的交通容量。验证了真实网络拓扑并不影响结果。半解析预测与数值模拟结果相符。