Prediction of Cascading Failures in Spatial Networks.
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.
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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 |
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Mentions: Networks, such as transportation networks and power grids, are similar to lattice networks on a macro scale. Though certain local portions of these real networks are different from such a lattice in that they cannot distinctively affect the behaviors of other portions of the network. Therefore, a lattice network is used in the experiments of this study. We created a 50 × 50 (2,500-node) undirected lattice network to represent a combined system, where each node represents a subsystem. Then, we randomly initialized the weight of each edge using a Gaussian distribution (1, 0.2) and ensured that there were no negative values. Node betweenness is an important and effective parameter to measure the importance of a node in a network and can describe the load of a node. In these experiments, the load of a single node is the number of shortest paths (i.e., minimum total weight) that pass through it. In this study, we used a fast algorithm based on the Dijkstra Algorithm to calculate all of the shortest paths in a network and the betweenness of each node [14]. To simulate an overload failure, we assumed that a node breaks down when its betweenness is 1 + α times higher than its initial value. When attackers seek to destroy a system, they typically focus on vital subsystems (i.e., central subsystems) or subsystems that tend to experience higher loads that will break down more easily. Thus, to start the cascading failure process, we initially attack the center 4 × 4 nodes and delete them from the network. Then, we recalculate the betweenness of each node that remains functional and delete the nodes whose betweenness is 1 + α times higher than its initial value. We repeat the above process until no node is deleted due to overload (Fig 1). |
View Article: PubMed Central - PubMed
Affiliation: School of Reliability and Systems Engineering, Beihang University, Beijing, China.
No MeSH data available.