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如何在保证网络传输效率的同时提高网络的吞吐量是目前研究的主要问题. 通过研究节点对数据包传递过程的引力作用,提出了一种具有引力约束的路由算法. 为检验算法的有效性,通过引入一个状态参数H, 利用由稳态到拥塞状态的指标流量相变值来度量网络的吞吐量, 同时利用数据包的最大传输时间〈Tmax〉与平均传输时间〈Tavg〉来分析网络的传输效率. 针对算法在不同引力约束条件下的路由情况进行了仿真.仿真结果表明, 若数据传递过程只考虑路径长度最短,则会导致网络吞吐量较低且流量分布极不均匀; 若只顾及等待时间最短,会导致传输路径过度迂回且大部分节点都会陷入拥塞状态; 同时考虑路径长度和等待时间的引力作用并选取适当引力的节点进行传递, 可以显著提高网络吞吐量并缓解网络的拥塞程度.How to guarantee the transport efficiency of the network and how to improve the network capacity are the main subject of the study presently. We investigate the gravity of the nodes to the transfer of data packets, and propose a routing method based on gravity constraint. In order to characterize the efficiency of the method, we introduce an order parameter H to measure the throughput of the network by a critical value of phase transition from free state to jammed state, and use the maximum travel time 〈Tmax〉 and the average travel time 〈Tavg〉 to test the transmission efficiency of the network. We simulate the network capacity under three different gravity constraints. Simulation results show that when only considering the path with shortest length, the network capacity is very small and the distribution of flow is extremely uneven; when only considering minimum waiting time, the excessive circuitous transfer of data packets occurs and most of the nodes will be in congestion state; when considering the gravity of path length and waiting time simultaneously and choosing a node with reasonable gravity, the network capacity will be improved greatly and the congestion level will be relieved to some extent.
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Keywords:
- complex network /
- gravity /
- throughput /
- congestion
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[29] Wang D, Yu H, Jing Y W, Jiang N, Zhang S Y 2009 Acta Phys. Sin. 58 6802 (in Chinese) [王丹, 于灏, 井元伟, 姜囡, 张嗣瀛 2009 物理学报 58 6802]
[30] Barabási A L, Albert R 1999 Science 286 509
[31] Arenas A, Díaz-Guilera A, Guimerá R 2001 Phys. Rev. Lett. 86 3196
[32] Danila B, Sun Y D, Bassler K E 2009 Phys. Rev. E 80 066116
-
[1] Newman M E J 2003 SIAM Rev. 45 167
[2] Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D U 2006 Phys. Rep. 424 175
[3] Melanie M 2006 Artificial Intelligence 170 1194
[4] Albert R, Barabási A L 2002 Rev. Mod. Phys. 74 47
[5] Strogatz S H 2001 Nature 410 268
[6] Li S B, Wu J J, Gao Z Y, Lin Y, Fu B B 2011 Acta Phys. Sin. 60 050701 (in Chinese) [李树彬, 吴建军, 高自友, 林勇, 傅白白 2011 物理学报 60 050701]
[7] Lin H, Wu C X 2007 Acta Phys. Sin. 56 4313 (in Chinese) [林海, 吴晨旭 2007 物理学报 56 4313]
[8] Szabo G, Szolnoki A, Vukov J 2009 Eur. Phys. Lett. 87 18007
[9] Gómez J G, Campillo M, Floríe1 L M, Moreno Y 2007 Phys. Rev. Lett. 98 1081
[10] Chowdhury D 2006 Physica A 372 84
[11] Koo J H, Ji D H, Won S C 2010 Appl. Math. Comput. 217 3916
[12] Dobson I, Carreras B A, Newman D E 2005 Prob. Eng. Inform. Sci. 19 15
[13] Hu K, Hu T, Tang Y 2010 Chin. Phys. B 19 080206
[14] Simonsen I, Buzna L, Peters K 2008 Phys. Rev. Lett. 100 2187
[15] Paster S R, Vespingnani A 2001 Phys. Rev. Lett. 86 3200
[16] Zager L, Verghese G 2009 Complexity 14 12
[17] Lancic A, Antulov F N, Sikic M 2011 Physica A 390 65
[18] Toenjes R, Masuda N, Kori H 2010 Chaos 20 033108
[19] Tu L L 2011 Chin. Phys. B 20 030504
[20] Yang J Z, Zhang M 2005 Chin. Phys. Lett. 22 2183
[21] Daniele D M, Luca D A, Ginestra B, Matteo M 2009 Phys. Rev. E 79 015101
[22] Pu C L, Pei W J 2010 Acta Phys. Sin. 59 3841 (in Chinese) [濮存来, 裴文江 2010 物理学报 59 3841]
[23] Yan G, Zhou T, Hu B, Fu Z Q 2006 Phys. Rev. E 73 046108
[24] Noh J D 2004 Phys. Rev. Lett. 92 11
[25] Yang S J 2005 Phys. Rev. E 71 016107
[26] Zhao L, Al-Dubai A Y, Min G Y 2011 Simul. Model. Prac. Theory 19 1415
[27] Chen H L, Liu Z X, Chen Z Q, Yuan Z Z 2009 Acta Phys. Sin. 58 6068 (in Chinese) [陈华良, 刘忠信, 陈增强, 袁著祉 2009 物理学报 58 6068]
[28] Echenique P, Gomez-Gardenes J, Moreno Y 2004 Phys. Rev. E 70 056105
[29] Wang D, Yu H, Jing Y W, Jiang N, Zhang S Y 2009 Acta Phys. Sin. 58 6802 (in Chinese) [王丹, 于灏, 井元伟, 姜囡, 张嗣瀛 2009 物理学报 58 6802]
[30] Barabási A L, Albert R 1999 Science 286 509
[31] Arenas A, Díaz-Guilera A, Guimerá R 2001 Phys. Rev. Lett. 86 3196
[32] Danila B, Sun Y D, Bassler K E 2009 Phys. Rev. E 80 066116
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