Analysis and Optimization of Robustness in Multiplex Flow Networks Against Cascading Failures

TL;DR

This paper introduces a multiplex flow network model to analyze and optimize robustness against cascading failures, proposing load-capacity strategies that enhance system resilience under different failure conditions.

Contribution

It develops a novel multiplex flow network framework with recursive equations and solutions for steady-state analysis, including optimal load-capacity allocation strategies for robustness.

Findings

Allocating excess capacity proportionally to mean load maximizes robustness.

Equal distribution of excess capacity among nodes improves resilience.

The framework can analyze various failure conditions and interdependencies.

Abstract

Networked systems are susceptible to cascading failures, where the failure of an initial set of nodes propagates through the network, often leading to system-wide failures. In this work, we propose a multiplex flow network model to study robustness against cascading failures triggered by random failures. The model is inspired by systems where nodes carry or support multiple types of flows, and failures result in the redistribution of flows within the same layer rather than between layers. To represent different types of interdependencies between the layers of the multiplex network, we define two cases of failure conditions: layer-independent overload and layer-influenced overload. We provide recursive equations and their solutions to calculate the steady-state fraction of surviving nodes, validate them through a set of simulation experiments, and discuss optimal load-capacity allocation strategies. Our results demonstrate that allocating the total excess capacity to each layer proportional to the mean effective load in the layer and distributing that excess capacity equally among the nodes within the layer ensures maximum robustness. The proposed framework for different failure conditions allows us to analyze the two overload conditions presented and can be extended to explore more complex interdependent relationships.