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Deliverable:  11 -  15  of  27   Pages:  1  2  3  4  5  6 
Deliverable D2.1.3[5] - A tool for network reliability analysis

Publsihed in Lecture Notes in Computer Science
by Springer Berlin / Heidelberg, Volume 4680/2007

The paper presents a tool for the reliability analysis of networks by means of two independent algorithms via the construction of a Binary Decision Diagrams. The ability of the algorithm to cope with different network topologies of increasing complexity has been tested in a series of preliminary experiments. The limits of the technique have been impinged and further experimentation is needed with more powerful computing facilities.

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Deliverable D2.1.3[6] - Efficient Response to cascading Disaster Spreading

This work studies the effectiveness of recovery strategies for a dynamic model of failure spreading in networks. These strategies control the distribution of resources based on information about the current network state and network topology. In order to assess their success, series of simulation experiments have been performed. The considered parameters of these experiments are the network topology, the response time delay, and the overall disposition of resources. Investigations are focused on the comparison of strategies for different scenarios and the determination of the most appropriate strategy. The importance of prompt response and the minimum sufficient quantity of resources are discussed as well.

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Deliverable D2.1.3[7] - Stationary Network Load Models Underestimate Vulnerability to cascading Failures

This work studies cascading failures in networks, and investigate the impact of transient dynamics caused by the redistribution of loads after an initial network failure (triggering event). It is found that considering instead the stationary states, as has been done in the past, may dramatically overestimate (by 80-95%) the robustness of the network. This is due to the transient oscillations or overshooting in the loads, when the flow dynamics adjusts to the new (remaining) network structure. Consequently, additional parts of the network may be overloaded and therefore fail before the stationary state is reached. The dynamical effects are strongest on links in the neighbourhood of broken links. This can cause failure waves in the network along neighbouring links, while the evaluation of the stationary solution predicts a different order of link failures.

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Deliverable D2.1.3[8] - Modelling of cascading Effects and efficient Response to Disaster spreading in Complex Networks

Authors: K. Peters, L. Buzna, D. Helbing
Published in International Journal of Critical Infrastructures, 2008 - Vol. 4, No.1/2

In order to assess cascading effects in directed networks this paper presents a model for the dynamics of failure spreading. The model combines network nodes as active, bistable elements and delayed interactions along directed links. By means of simulations the dynamics behaviour for generic sample networks has been studied. Besides the evaluation of failure cascades, for which we observe a critical threshold for the undamped spreading of failures in a network we simulated the effect of different strategies for the management of spreading disasters.

Our recovery strategies are based on the assumption that the interaction structure of the challenged network remains unchanged, while additional resources for mitigation actions, improving the recovery capacities of system components, can be distributed over the network.

The simulations clearly demonstrate that the topology of a network is a crucial factor both for the behaviour under external disturbances as well as for the optimality of different strategies to cope with an evolving disaster. Our model may be used to improve disaster preparedness and anticipative disaster response management.

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Deliverable D2.1.3[9] - Optimized Response to cascading Failures in Complex Networks

Published in Risk, Reliability and Societal Safety
Editors: T.Aven, J.E. Vinnem
Authors: L. Buzna, K. Peters, D. Helbing
Published by Taylor & Francis Group , London, 2007
ISBN 978-0-415-44786-7

This work studies the dynamic evolution of failures for sample networks using a recently developed generic model for failure spreading mechanisms (Buzna et al. 2006). Based on computer simulations of failure spreading scenarios (Buzna et al. 2006a), we investigate in this paper the efficiency of different strategies to fight the spreading of disasters. These disaster response strategies control the distribution of resources based on different information about the current network state and network topology. Besides the availability of such vital information, we have considered the response time for mitigation actions and the overall disposition of resources as model parameters.

Our investigations are focused on the comparison of strategies under different conditions. The results indicate that, for certain parameter regions the success of recovery measures and disaster containment depends crucially on the chosen management strategy. We demonstrate that, under certain circumstances, optimization techniques can be employed to improve the performance of recovery strategies. Therefore, our studies may be used to improve disaster preparedness and anticipative disaster response management.

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