Understanding and Modeling the Internet Topology: Economics and Evolution Perspective

In this paper, we seek to understand the intrinsic reasons for the well-known phenomenon of heavy-tailed degree in the Internet AS graph and argue that in contrast to traditional models based on preferential attachment and centralized optimization, the Pareto degree of the Internet can be explained by the evolution of wealth associated with each ISP. The proposed topology model utilizes a simple multiplicative stochastic process that determines each ISP's wealth at different points in time and several ¿maintenance¿ rules that keep the degree of each node proportional to its wealth. Actual link formation is determined in a decentralized fashion based on random walks, where each ISP individually decides when and how to increase its degree. Simulations show that the proposed model, which we call Wealth-based Internet Topology (WIT), produces scale-free random graphs with tunable exponent ¿ and high clustering coefficients (between 0.35 and 0.5) that stay invariant as the size of the graph increases. This evolution closely mimics that of the Internet observed since 1997.

[1]  Walter Willinger,et al.  In search of the elusive ground truth: the internet's as-level connectivity structure , 2008, SIGMETRICS '08.

[2]  Michalis Faloutsos,et al.  A Systematic Framework for Unearthing the Missing Links: Measurements and Impact , 2007, NSDI.

[3]  Arun Venkataramani,et al.  iPlane: an information plane for distributed services , 2006, OSDI '06.

[4]  Abhijit Kar Gupta Models of wealth distributions: a perspective , 2006, physics/0604161.

[5]  Walter Willinger,et al.  To Peer or Not to Peer: Modeling the Evolution of the Internet's AS-Level Topology , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[6]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[7]  Yuval Shavitt,et al.  DIMES: let the internet measure itself , 2005, CCRV.

[8]  Arnold L. Rosenberg,et al.  Comparing the structure of power-law graphs and the Internet AS graph , 2004, Proceedings of the 12th IEEE International Conference on Network Protocols, 2004. ICNP 2004..

[9]  Shi Zhou,et al.  Accurately modeling the Internet topology , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  S. Gorman,et al.  Spatial Small Worlds: New Geographic Patterns for an Information Economy , 2003, cond-mat/0310426.

[11]  Xiang Li,et al.  A local-world evolving network model , 2003 .

[12]  Walter Willinger,et al.  Internet connectivity at the AS-level: an optimization-driven modeling approach , 2003, MoMeTools '03.

[13]  S. N. Dorogovtsev Networks with given correlations , 2003 .

[14]  S. Havlin,et al.  Geographical embedding of scale-free networks , 2003, cond-mat/0301504.

[15]  Donald F. Towsley,et al.  On distinguishing between Internet power law topology generators , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[16]  Ibrahim Matta,et al.  On the geographic location of internet resources , 2002, IMW '02.

[17]  F. Chung,et al.  Connected Components in Random Graphs with Given Expected Degree Sequences , 2002 .

[18]  S. Shenker,et al.  Network topology generators: degree-based vs. structural , 2002, SIGCOMM '02.

[19]  Christos H. Papadimitriou,et al.  On the Eigenvalue Power Law , 2002, RANDOM.

[20]  L. Sander,et al.  Geography in a scale-free network model. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[21]  Christos H. Papadimitriou,et al.  Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet , 2002, ICALP.

[22]  S. Redner,et al.  Finiteness and fluctuations in growing networks , 2002, cond-mat/0207107.

[23]  M. Newman Assortative mixing in networks. , 2002, Physical review letters.

[24]  I. Sokolov,et al.  Evolving networks with disadvantaged long-range connections. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  K. Goh,et al.  Fluctuation-driven dynamics of the internet topology. , 2001, Physical review letters.

[26]  V. Eguíluz,et al.  Highly clustered scale-free networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[27]  Hawoong Jeong,et al.  Modeling the Internet's large-scale topology , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Jie Wu,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2003 .

[29]  Azer Bestavros,et al.  Small-World Internet Topologies: Possible Causes and Implications on Scalability of End-System Multicast , 2002 .

[30]  Bernardo A. Huberman,et al.  Intentional Walks on Scale Free Small Worlds , 2001, ArXiv.

[31]  Damien Magoni,et al.  Analysis of the autonomous system network topology , 2001, CCRV.

[32]  Michael William Newman,et al.  The Laplacian spectrum of graphs , 2001 .

[33]  G. J. Rodgers,et al.  Degree distributions of growing networks. , 2000, Physical review letters.

[34]  S. Bornholdt,et al.  World Wide Web scaling exponent from Simon's 1955 model. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  T. Erlebach,et al.  A Spectral Analysis of the Internet Topology , 2001 .

[36]  Lixin Gao,et al.  On inferring autonomous system relationships in the Internet , 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

[37]  Albert,et al.  Topology of evolving networks: local events and universality , 2000, Physical review letters.

[38]  Fan Chung Graham,et al.  A random graph model for massive graphs , 2000, STOC '00.

[39]  Ibrahim Matta,et al.  On the origin of power laws in Internet topologies , 2000, CCRV.

[40]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[41]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[42]  J M Carlson,et al.  Highly optimized tolerance: a mechanism for power laws in designed systems. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[43]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[44]  S. Redner How popular is your paper? An empirical study of the citation distribution , 1998, cond-mat/9804163.

[45]  D. Sornette Multiplicative processes and power laws , 1997, cond-mat/9708231.

[46]  Fan Chung,et al.  Spectral Graph Theory , 1996 .

[47]  S. Solomon,et al.  POWER LAWS ARE LOGARITHMIC BOLTZMANN LAWS , 1996, adap-org/9607001.

[48]  S. Solomon,et al.  Dynamical Explanation For The Emergence Of Power Law In A Stock Market Model , 1996 .

[49]  Bruce A. Reed,et al.  A Critical Point for Random Graphs with a Given Degree Sequence , 1995, Random Struct. Algorithms.

[50]  Per O. Seglen,et al.  The Skewness of Science , 1992, J. Am. Soc. Inf. Sci..

[51]  B. Mohar THE LAPLACIAN SPECTRUM OF GRAPHS y , 1991 .

[52]  Ronald W. Wolff,et al.  Stochastic Modeling and the Theory of Queues , 1989 .

[53]  BERNARD M. WAXMAN,et al.  Routing of multipoint connections , 1988, IEEE J. Sel. Areas Commun..

[54]  Herbert A. Simon,et al.  Some Further Notes on a Class of Skew Distribution Functions , 1960, Inf. Control..

[55]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .