An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

doi:10.1088/1674-1056/22/5/058902

中国物理B ›› 2013, Vol. 22 ›› Issue (5): 58902-058902.doi: 10.1088/1674-1056/22/5/058902

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇下一篇

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

沈毅

College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China

收稿日期:2012-08-27 修回日期:2012-10-16 出版日期:2013-04-01 发布日期:2013-04-01
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 60672095) and the Youth Science and Technology Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

Shen Yi (沈毅)

College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China

Received:2012-08-27 Revised:2012-10-16 Online:2013-04-01 Published:2013-04-01
Contact: Shen Yi E-mail:shen_yi1979@njau.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 60672095) and the Youth Science and Technology Innovation Fund of Nanjing Agricultural University, China (Grant No. KJ2010024).

摘要/Abstract

摘要： In this paper, we propose an adaptive strategy based on linear-prediction of queue length to minimize congestion in Barabási-Albert (BA) scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

关键词: linear prediction, congestion, networks

Abstract: In this paper, we propose an adaptive strategy based on linear-prediction of queue length to minimize congestion in Barabási-Albert (BA) scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

Key words: linear prediction, congestion, networks

中图分类号: (Networks and genealogical trees)

89.75.Hc

02.50.-r (Probability theory, stochastic processes, and statistics)

沈毅. An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks[J]. 中国物理B, 2013, 22(5): 58902-058902.

Shen Yi (沈毅). An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks[J]. Chin. Phys. B, 2013, 22(5): 58902-058902.

参考文献 33

[1]	Faloutsos M, Faloutsos P and Faloutsos C 1999 Compu. Commun. Rev. 29 251
[2]	Bono F, Gutiérrez E and Poljansek K 2010 Physica A 389 5287
[3]	Tadić B, Rodgers G J and Thurner S 2007 Int. J. Bifurcat. Chaos Appl. Sci. Eng. 17 2363
[4]	Boccaletti S, Latorab V, Moreno Y, Chavez M and Hwang D U 2006 Phys. Rep. 424 175
[5]	Li S M, Xu X H and Meng L H 2012 Chin. Phys. B 21 088901
[6]	Wang D L, Yu Z G and Anh V 2012 Chin. Phys. B 21 080504
[7]	Zou A Q, Li Y and Wu J 2010 Chin. Phys. Lett. 27 068901
[8]	Newmam M E J 2010 Networks: an Introduction (Oxford: Oxford University Press)
[9]	Newman M E J 2003 SIAM Rev. 45 167
[10]	Albert R and Barabási A L 2002 Rev. Mod. Phys. 74 48
[11]	Li W, Yang J Y and Hadden W C 2009 Europhys. Lett. 88 68007
[12]	Sun J and Avraham D B 2010 Phys. Rev. E 82 0161109
[13]	Sreenivasan S, Cohen R, Lopez E, Toroczkai Z and Stanley H E 2007 Phys. Rev. E 75 036105
[14]	Guimerá R, Arenas A, Guilera A D and Giralt F 2002 Phys. Rev. E 66 026704
[15]	Rosato V, Issacharoff L, Meloni S, Caligiore D and Tiriticco F 2008 Physica A 387 1689
[16]	Scellato S, Fortuna L, Frasca M, Gardeñes J G and Latora V 2010 Eur. Phys. J. B 73 303
[17]	Guimerá R, Guilera A D, Redondo F V, Cabrales A and Arenas A 2002 Phys. Rev. Lett. 89 248701
[18]	Wang W X, Wang B H, Yin C Y, Xie Y B and Zhou T 2006 Phys. Rev. E 73 026111
[19]	Goh K I, Kahng B and Kim D 2001 Phys. Rev. Lett. 87 278701
[20]	Echenique P, Gardeñes J G and Moreno Y 2004 Phys. Rev. E 70 056105
[21]	Yan G, Zhou T, Hu B, Fu Z Q and Wang B H 2006 Phys. Rev. E 73 046108
[22]	Liu Z, Hu M B, Jiang R, Wang W X and Wu Q S 2007 Phys. Rev. E 76 037101
[23]	Huang W and Chow T W S 2009 Chaos 19 043124
[24]	Kim K, Kahng B and Kim D 2009 Europhys. Lett. 86 58002
[25]	Petri G, Jensen H J and Polak J W 2009 Europhys. Lett. 88 20010
[26]	Noh J D 2004 Phys. Rev. Lett. 92 118701
[27]	Hua D Y and Wang L Y 2010 Chin. Phys. Lett. 27 098901
[28]	Oppenheim A V, Willsky A S and Nawab S H 1997 Signals and Systems (London: Pearson Higher Isia Education Press)
[29]	Biggs N L 1974 Algebraic Graph Theory (Cambridge: Cambridge University Press)
[30]	Shen Y, Pei W J, Wang K, Li T and Wang S P 2008 Physica A 387 6663
[31]	Albert R and Barabási A L 1999 Science 286 509
[32]	Danila B, Marsh J A and Bassler K E 2007 Chaos 17 026102
[33]	Sreenivasan S, Cohen R, López E, Toroczkai Z and Stanley H E 2007 Phys. Rev. E 75 036105

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

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