10,383 research outputs found
Modeling the Internet's Large-Scale Topology
Network generators that capture the Internet's large-scale topology are
crucial for the development of efficient routing protocols and modeling
Internet traffic. Our ability to design realistic generators is limited by the
incomplete understanding of the fundamental driving forces that affect the
Internet's evolution. By combining the most extensive data on the time
evolution, topology and physical layout of the Internet, we identify the
universal mechanisms that shape the Internet's router and autonomous system
level topology. We find that the physical layout of nodes form a fractal set,
determined by population density patterns around the globe. The placement of
links is driven by competition between preferential attachment and linear
distance dependence, a marked departure from the currently employed exponential
laws. The universal parameters that we extract significantly restrict the class
of potentially correct Internet models, and indicate that the networks created
by all available topology generators are significantly different from the
Internet
Graph Annotations in Modeling Complex Network Topologies
The coarsest approximation of the structure of a complex network, such as the
Internet, is a simple undirected unweighted graph. This approximation, however,
loses too much detail. In reality, objects represented by vertices and edges in
such a graph possess some non-trivial internal structure that varies across and
differentiates among distinct types of links or nodes. In this work, we
abstract such additional information as network annotations. We introduce a
network topology modeling framework that treats annotations as an extended
correlation profile of a network. Assuming we have this profile measured for a
given network, we present an algorithm to rescale it in order to construct
networks of varying size that still reproduce the original measured annotation
profile.
Using this methodology, we accurately capture the network properties
essential for realistic simulations of network applications and protocols, or
any other simulations involving complex network topologies, including modeling
and simulation of network evolution. We apply our approach to the Autonomous
System (AS) topology of the Internet annotated with business relationships
between ASs. This topology captures the large-scale structure of the Internet.
In depth understanding of this structure and tools to model it are cornerstones
of research on future Internet architectures and designs. We find that our
techniques are able to accurately capture the structure of annotation
correlations within this topology, thus reproducing a number of its important
properties in synthetically-generated random graphs
Chinese Internet AS-level Topology
We present the first complete measurement of the Chinese Internet topology at
the autonomous systems (AS) level based on traceroute data probed from servers
of major ISPs in mainland China. We show that both the Chinese Internet AS
graph and the global Internet AS graph can be accurately reproduced by the
Positive-Feedback Preference (PFP) model with the same parameters. This result
suggests that the Chinese Internet preserves well the topological
characteristics of the global Internet. This is the first demonstration of the
Internet's topological fractality, or self-similarity, performed at the level
of topology evolution modeling.Comment: This paper is a preprint of a paper submitted to IEE Proceedings on
Communications and is subject to Institution of Engineering and Technology
Copyright. If accepted, the copy of record will be available at IET Digital
Librar
A critical look at power law modelling of the Internet
This paper takes a critical look at the usefulness of power law models of the
Internet. The twin focuses of the paper are Internet traffic and topology
generation. The aim of the paper is twofold. Firstly it summarises the state of
the art in power law modelling particularly giving attention to existing open
research questions. Secondly it provides insight into the failings of such
models and where progress needs to be made for power law research to feed
through to actual improvements in network performance.Comment: To appear Computer Communication
Systematic Topology Analysis and Generation Using Degree Correlations
We present a new, systematic approach for analyzing network topologies. We
first introduce the dK-series of probability distributions specifying all
degree correlations within d-sized subgraphs of a given graph G. Increasing
values of d capture progressively more properties of G at the cost of more
complex representation of the probability distribution. Using this series, we
can quantitatively measure the distance between two graphs and construct random
graphs that accurately reproduce virtually all metrics proposed in the
literature. The nature of the dK-series implies that it will also capture any
future metrics that may be proposed. Using our approach, we construct graphs
for d=0,1,2,3 and demonstrate that these graphs reproduce, with increasing
accuracy, important properties of measured and modeled Internet topologies. We
find that the d=2 case is sufficient for most practical purposes, while d=3
essentially reconstructs the Internet AS- and router-level topologies exactly.
We hope that a systematic method to analyze and synthesize topologies offers a
significant improvement to the set of tools available to network topology and
protocol researchers.Comment: Final versio
Large-scale topological and dynamical properties of Internet
We study the large-scale topological and dynamical properties of real
Internet maps at the autonomous system level, collected in a three years time
interval. We find that the connectivity structure of the Internet presents
average quantities and statistical distributions settled in a well-defined
stationary state. The large-scale properties are characterized by a scale-free
topology consistent with previous observations. Correlation functions and
clustering coefficients exhibit a remarkable structure due to the underlying
hierarchical organization of the Internet. The study of the Internet time
evolution shows a growth dynamics with aging features typical of recently
proposed growing network models. We compare the properties of growing network
models with the present real Internet data analysis.Comment: 13 pages, 15 eps figure
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