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Prediction of Cascading Failures in Spatial Networks.

Shunkun Y, Jiaquan Z, Dan L - PLoS ONE (2016)

Bottom Line: Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems.Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance.

View Article: PubMed Central - PubMed

Affiliation: School of Reliability and Systems Engineering, Beihang University, Beijing, China.

ABSTRACT
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods. We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.

No MeSH data available.


Related in: MedlinePlus

Average error of different distance errors with different values of α.
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pone.0153904.g006: Average error of different distance errors with different values of α.

Mentions: Because there are 23 predictors for each α, and each predictor has its own error, we calculated the average error of different predictors for each α to fully describe the accuracy of the predictors with different values of α (Fig 6). The average errors of different models are shown to decrease as α increases and show similar trends. The errors decrease as α increases because nodes are sensitive to overload when α is small, and the network changes rapidly with time. Many failures occur in a single step, which increases the randomness of the cascading failure process. The similar decreasing trends of the different models also indicate that each model has reached an approximately optimal solution.


Prediction of Cascading Failures in Spatial Networks.

Shunkun Y, Jiaquan Z, Dan L - PLoS ONE (2016)

Average error of different distance errors with different values of α.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4836660&req=5

pone.0153904.g006: Average error of different distance errors with different values of α.
Mentions: Because there are 23 predictors for each α, and each predictor has its own error, we calculated the average error of different predictors for each α to fully describe the accuracy of the predictors with different values of α (Fig 6). The average errors of different models are shown to decrease as α increases and show similar trends. The errors decrease as α increases because nodes are sensitive to overload when α is small, and the network changes rapidly with time. Many failures occur in a single step, which increases the randomness of the cascading failure process. The similar decreasing trends of the different models also indicate that each model has reached an approximately optimal solution.

Bottom Line: Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems.Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics.We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance.

View Article: PubMed Central - PubMed

Affiliation: School of Reliability and Systems Engineering, Beihang University, Beijing, China.

ABSTRACT
Cascading overload failures are widely found in large-scale parallel systems and remain a major threat to system reliability; therefore, they are of great concern to maintainers and managers of different systems. Accurate cascading failure prediction can provide useful information to help control networks. However, for a large, gradually growing network with increasing complexity, it is often impractical to explore the behavior of a single node from the perspective of failure propagation. Fortunately, overload failures that propagate through a network exhibit certain spatial-temporal correlations, which allows the study of a group of nodes that share common spatial and temporal characteristics. Therefore, in this study, we seek to predict the failure rates of nodes in a given group using machine-learning methods. We simulated overload failure propagations in a weighted lattice network that start with a center attack and predicted the failure percentages of different groups of nodes that are separated by a given distance. The experimental results of a feedforward neural network (FNN), a recurrent neural network (RNN) and support vector regression (SVR) all show that these different models can accurately predict the similar behavior of nodes in a given group during cascading overload propagation.

No MeSH data available.


Related in: MedlinePlus