12,036 research outputs found
Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependency
Increased coupling between critical infrastructure networks, such as power
and communication systems, will have important implications for the reliability
and security of these systems. To understand the effects of power-communication
coupling, several have studied interdependent network models and reported that
increased coupling can increase system vulnerability. However, these results
come from models that have substantially different mechanisms of cascading,
relative to those found in actual power and communication networks. This paper
reports on two sets of experiments that compare the network vulnerability
implications resulting from simple topological models and models that more
accurately capture the dynamics of cascading in power systems. First, we
compare a simple model of topological contagion to a model of cascading in
power systems and find that the power grid shows a much higher level of
vulnerability, relative to the contagion model. Second, we compare a model of
topological cascades in coupled networks to three different physics-based
models of power grids coupled to communication networks. Again, the more
accurate models suggest very different conclusions. In all but the most extreme
case, the physics-based power grid models indicate that increased
power-communication coupling decreases vulnerability. This is opposite from
what one would conclude from the coupled topological model, in which zero
coupling is optimal. Finally, an extreme case in which communication failures
immediately cause grid failures, suggests that if systems are poorly designed,
increased coupling can be harmful. Together these results suggest design
strategies for reducing the risk of cascades in interdependent infrastructure
systems
Ricci Curvature of the Internet Topology
Analysis of Internet topologies has shown that the Internet topology has
negative curvature, measured by Gromov's "thin triangle condition", which is
tightly related to core congestion and route reliability. In this work we
analyze the discrete Ricci curvature of the Internet, defined by Ollivier, Lin,
etc. Ricci curvature measures whether local distances diverge or converge. It
is a more local measure which allows us to understand the distribution of
curvatures in the network. We show by various Internet data sets that the
distribution of Ricci cuvature is spread out, suggesting the network topology
to be non-homogenous. We also show that the Ricci curvature has interesting
connections to both local measures such as node degree and clustering
coefficient, global measures such as betweenness centrality and network
connectivity, as well as auxilary attributes such as geographical distances.
These observations add to the richness of geometric structures in complex
network theory.Comment: 9 pages, 16 figures. To be appear on INFOCOM 201
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