Design Robust Networks against Overload-Based Cascading Failures

Cascading failures are an interesting phenomenon in the study of complex networks and have attracted great attention. Examples of cascading failures include disease epidemics, traffic congestion, and electrical power system blackouts. In these systems, if external shocks or excess loads at some nodes are propagated to other connected nodes because of failure, a domino effect often occurs with disastrous consequences. Therefore, how to prevent cascading failures in complex networks is emerging as an important issue. A vast amount of research has attempted to design large networked infrastructures with the capability to withstand failures and fluctuations; this can be thought of as an optimal design task. In this paper, a cascading failure on an overload-based model was studied and a novel core-periphery network topology was heuristically designed to mitigate the damage of cascading failures. Using the Largest Connected Component after a sequence of failures as the network robustness measure, numerical simulations show that the proposed network, which consists of a complete core of connected hub nodes and periphery nodes connecting to the core, is the least susceptible to cascading failures compared to other types of networks.

[1]  Massimo Marchiori,et al.  Model for cascading failures in complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[3]  Adilson E Motter Cascade control and defense in complex networks. , 2004, Physical review letters.

[4]  Christos Faloutsos,et al.  Epidemic spreading in real networks: an eigenvalue viewpoint , 2003, 22nd International Symposium on Reliable Distributed Systems, 2003. Proceedings..

[5]  Hans J. Herrmann,et al.  Mitigation of malicious attacks on networks , 2011, Proceedings of the National Academy of Sciences.

[6]  Ian Dobson,et al.  Evidence for self-organized criticality in electric power system blackouts , 2001, Proceedings of the 34th Annual Hawaii International Conference on System Sciences.

[7]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[8]  D. Newth,et al.  Optimizing complex networks for resilience against cascading failure , 2007 .

[9]  Franklin Allen,et al.  Financial Contagion , 2000, Journal of Political Economy.

[10]  Paul L. Krapivsky,et al.  A statistical physics perspective on Web growth , 2002, Comput. Networks.

[11]  R. Durrett Random Graph Dynamics: References , 2006 .

[12]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[13]  Franklin Allen,et al.  Financial Contagion Journal of Political Economy , 1998 .

[14]  Adilson E Motter,et al.  Cascade-based attacks on complex networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  A. Barabasi,et al.  Scale-free characteristics of random networks: the topology of the world-wide web , 2000 .

[16]  M. L. Sachtjen,et al.  Disturbances in a power transmission system , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[17]  H. Yuan A bound on the spectral radius of graphs , 1988 .

[18]  Bing Wang,et al.  A high-robustness and low-cost model for cascading failures , 2007, 0704.0345.

[19]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[20]  Akira Namatame,et al.  Mitigating Cascading Failure with Adaptive Networking , 2015, New Math. Nat. Comput..