16,047 research outputs found
Generating Representative ISP Technologies From First-Principles
Understanding and modeling the factors that underlie the growth and evolution of network topologies are basic questions that impact capacity planning, forecasting, and protocol research. Early topology generation work focused on generating network-wide connectivity maps, either at the AS-level or the router-level, typically with an eye towards reproducing abstract properties of observed topologies. But recently, advocates of an alternative "first-principles" approach question the feasibility of realizing representative topologies with simple generative models that do not explicitly incorporate real-world constraints, such as the relative costs of router configurations, into the model. Our work synthesizes these two lines by designing a topology generation mechanism that incorporates first-principles constraints. Our goal is more modest than that of constructing an Internet-wide topology: we aim to generate representative topologies for single ISPs. However, our methods also go well beyond previous work, as we annotate these topologies with representative capacity and latency information. Taking only demand for network services over a given region as input, we propose a natural cost model for building and interconnecting PoPs and formulate the resulting optimization problem faced by an ISP. We devise hill-climbing heuristics for this problem and demonstrate that the solutions we obtain are quantitatively similar to those in measured router-level ISP topologies, with respect to both topological properties and fault-tolerance
Scalable Routing Easy as PIE: a Practical Isometric Embedding Protocol (Technical Report)
We present PIE, a scalable routing scheme that achieves 100% packet delivery
and low path stretch. It is easy to implement in a distributed fashion and
works well when costs are associated to links. Scalability is achieved by using
virtual coordinates in a space of concise dimensionality, which enables greedy
routing based only on local knowledge. PIE is a general routing scheme, meaning
that it works on any graph. We focus however on the Internet, where routing
scalability is an urgent concern. We show analytically and by using simulation
that the scheme scales extremely well on Internet-like graphs. In addition, its
geometric nature allows it to react efficiently to topological changes or
failures by finding new paths in the network at no cost, yielding better
delivery ratios than standard algorithms. The proposed routing scheme needs an
amount of memory polylogarithmic in the size of the network and requires only
local communication between the nodes. Although each node constructs its
coordinates and routes packets locally, the path stretch remains extremely low,
even lower than for centralized or less scalable state-of-the-art algorithms:
PIE always finds short paths and often enough finds the shortest paths.Comment: This work has been previously published in IEEE ICNP'11. The present
document contains an additional optional mechanism, presented in Section
III-D, to further improve performance by using route asymmetry. It also
contains new simulation result
Global Networks of Trade and Bits
Considerable efforts have been made in recent years to produce detailed
topologies of the Internet. Although Internet topology data have been brought
to the attention of a wide and somewhat diverse audience of scholars, so far
they have been overlooked by economists. In this paper, we suggest that such
data could be effectively treated as a proxy to characterize the size of the
"digital economy" at country level and outsourcing: thus, we analyse the
topological structure of the network of trade in digital services (trade in
bits) and compare it with that of the more traditional flow of manufactured
goods across countries. To perform meaningful comparisons across networks with
different characteristics, we define a stochastic benchmark for the number of
connections among each country-pair, based on hypergeometric distribution.
Original data are thus filtered by means of different thresholds, so that we
only focus on the strongest links, i.e., statistically significant links. We
find that trade in bits displays a sparser and less hierarchical network
structure, which is more similar to trade in high-skill manufactured goods than
total trade. Lastly, distance plays a more prominent role in shaping the
network of international trade in physical goods than trade in digital
services.Comment: 25 pages, 6 figure
Community structure detection in the evolution of the United States airport network
This is the post-print version of the Article. Copyright © 2013 World Scientific PublishingThis paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration
A GDP-driven model for the binary and weighted structure of the International Trade Network
Recent events such as the global financial crisis have renewed the interest
in the topic of economic networks. One of the main channels of shock
propagation among countries is the International Trade Network (ITN). Two
important models for the ITN structure, the classical gravity model of trade
(more popular among economists) and the fitness model (more popular among
networks scientists), are both limited to the characterization of only one
representation of the ITN. The gravity model satisfactorily predicts the volume
of trade between connected countries, but cannot reproduce the observed missing
links (i.e. the topology). On the other hand, the fitness model can
successfully replicate the topology of the ITN, but cannot predict the volumes.
This paper tries to make an important step forward in the unification of those
two frameworks, by proposing a new GDP-driven model which can simultaneously
reproduce the binary and the weighted properties of the ITN. Specifically, we
adopt a maximum-entropy approach where both the degree and the strength of each
node is preserved. We then identify strong nonlinear relationships between the
GDP and the parameters of the model. This ultimately results in a weighted
generalization of the fitness model of trade, where the GDP plays the role of a
`macroeconomic fitness' shaping the binary and the weighted structure of the
ITN simultaneously. Our model mathematically highlights an important asymmetry
in the role of binary and weighted network properties, namely the fact that
binary properties can be inferred without the knowledge of weighted ones, while
the opposite is not true
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