A limited resource model of fault-tolerant capability against cascading failure of complex network

Abstract

Abstract. We propose a novel capacity model for complex networks against cascading failure. In this model, vertices with both higher loads and larger degrees should be paid more extra capacities, i.e. the allocation of extra capacity on vertex i will be proportional to k γ i , where ki is the degree of vertex i and γ > 0 is a free parameter. We have applied this model on Barabási-Albert network as well as two real transportation networks, and found that under the same amount of available resource, this model can achieve better network robustness than previous models.

A Novel Load Capacity Model with a Tunable Proportion of Load Redistribution against Cascading Failures

“…To be more practical and reduce the collapse scale, scholars have put forward many cascading failures model based on ML model. Zhou et al [8] deem that degrees of nodes in networks can to some extent reflect the processing ability and let nodes with both higher loads and larger degrees acquire more extra capacities. Sun et al [9] propose a new matching model of capacity by developing a profit function to defence cascading failures on artificially created scale-free networks and the real network structure of the North American power grid.…”

“…And the load redistribution proportion can be seen as a function of initial loads, where = ( )/ ∑ ∈Ω ( ) and ( ) = . Actually, the load redistribution proportion [8][9][10][11][12][13][14] depends on the initial loads that reflect the load processing ability to some extent. Duan et al [17,18] explore the critical thresholds of scale-free networks against cascading failures and spatiotemporal tolerance after a fraction of nodes attacked with a tunable load redistribution model that can tune the load redistribution range and heterogeneity of the broken nodes.…”

network resource reallocation strategy based on an improved capacity-load model

“…Optimal resource allocation has been used as a usual way to improve the network robustness to cascading failures [10,22,31]. Specifically, overloads (or congestion) on network nodes (edges) are one of the main reasons for triggering cascading failures in networks in reality.…”

Research on Defense Model of Cascading Failures in Complex Information System Networks

“…on network disaster spreading by using weights neural network model , and discussed the effectiveness of six coping strategies and optimal strategy in detail. A lot of control and prevention strategies for cascade behavior have been proposed on how to get the best ability to resist cascade reaction by limited resources [3,4,5], OuYang and others [6] study the cascade dynamics of a complex system with redundant resources by using the node dynamics equations in response to disasters and accidents in infrastructure networks, and compare the effectiveness of different resource allocation strategies respectively.…”