HY-483 Presentation On power law relationships of the internet topology A First Principles Approach to Understanding the Internet’s Router-level Topology On natural mobility models

On power law relationships of the internet topology Michalis Faloutsos U.C. Riverside Dept. of Comp. Science Petros Faloutsos U. of Toronto Dept. of Comp. Science Christos Faloutsos Carnegie Mellon Univ. Dept. of Comp. Science

Previous work Heavy tailed distributions used to describe LAN and WAN traffic Power laws describe WWW traffic There hasn't been any work on power laws with respect to topology.

Dataset & Methodology Three inter-domain level instances of the internet (97-98), in which the topology grew by 45%. Router-level instance of the internet in 1995 Min,Max and Means fail to describe skewed distributions Linear Regression & correlation coeficients, to fit a plot to a line

First power law: the rank exponent R Lemma1: Lemma2:

The rank exponent in AS and router level

Second power law: the outdegree exponent O Test of the realism of a graph metric follows a power law exponent is close to realistic numbers

The outdegree exponent O

Approximation: the hop-plot exponent H Lemma 3: Definition deff: Lemma 4: O (d·h^H) Previous definition O(d^h)

The hop-plot exponent H

Average Neighborhood size

Third power law: the eigen exponent ε The eigen value λ of a graph is related with the graph's adjacency matrix A (Ax = λx) diameter the number of edges the number of spanning trees the number of CCs the number of walks of a certain length between vertices

The eigenvalues exponent ε

Contributions-Speculations Exponents describe different families of graphs Deff improved calculation complexity from previous O(d^h) to O(d·h^H) What about 9-20% error in the computation of E?

A First Principles Approach to Understanding the Internet’s Router-level Topology Lun Li California Institute of Technology David Alderson California Institute of Technology Walter Willinger AT&T Labs Research John Doyle California Institute of Technology

Previous work Random graphs Hierarchical structural models Degree-based topology generators. Preferential attachment General model of random graphs (GRG) Power Law Random Graph (PLRG)

A First Principles Approach Technology constraints Feasible region Economic considerations End user demands Heuristically optimal networks Abilene and CENIC

Evaluation of a topology Current metrics are inadequate and lack a direct networking interpretation Node degree distribution Expansion Resilience Distortion Hierarchy Proposals Performance related Likelihood-related metrics

Abilene-CENIC

Comparison of simulated topologies with power law degree distributions and different features

Performane-Likelihood Comparison

Contributions-Speculations Different graphs generated by degree-based models, with average likelihood, are Difficult to be distinguished with macroscopic statistic metrics Yield low performance Simple heuristically design topologies High performance Efficiency Robustness not incorporated in the analysis Validation with real data

On natural mobility models Vincent Borrel Marcelo Dias de Amorim Serge Fdida LIP6/CNRS – Université Pierre et Marie Curie 8, rue du Capitaine Scott – – Paris – France

Previous work Individual mobility models Random Walk Random Waypoint Random Direction model Boundless Simulation Gauss-Markov model, City Section model, Group mobility models Reference Point model, Exponential Correlated Pursue model

Aspects of real-life networks Scale free property and high clustering coefficient Biology Computer networks Sociology

Proposal: Gathering Mobility (1/2) Why? Current group mobility models Rigid Unrealistic Match reality using scale free distributions Human behavior Research on Ad-hoc inter-contacts

Proposal: Gathering Mobility (2/2) The model Individuals Cycle behavior Attractors Appear-dissapear Probability an individual to choose an attractor Attractiveness of an attractor

Experiment Scale-free spatial distribution Scale-free Population growth

Contributions-Speculations A succesive merge of individual and group behavior: Individual movement No explicit grouping Vs Strong collective behaviour Influence by other individuals Gathering around centers of interest of varying popularity levels Determination of maintenance of this distribution in case of population decrease and renewal