# Prevention and Mitigation of Catastrophic Failures in Demand-Supply Interdependent Networks

## TL;DR

This paper introduces a model for demand-supply interdependent networks, analyzing cascading failures caused by resource/load fluctuations, and proposes resource allocation and network adaptation strategies to prevent and mitigate such failures.

## Contribution

It develops a generic model for demand-supply networks, identifies stress mechanisms, and proposes optimal resource allocation and adaptation methods to enhance network robustness.

## Key findings

- Quantified network robustness under different resource configurations.
- Designed optimal resource allocation strategies for various fluctuation scenarios.
- Proposed network adaptation mechanisms to contain ongoing failures.

## Abstract

We propose a generic system model for a special category of interdependent networks, demand-supply networks, in which the demand and the supply nodes are associated with heterogeneous loads and resources, respectively. Our model sheds a light on a unique cascading failure mechanism induced by resource/load fluctuations, which in turn opens the door to conducting stress analysis on interdependent networks. Compared to the existing literature mainly concerned with the node connectivity, we focus on developing effective resource allocation methods to prevent these cascading failures from happening and to mitigate/confine them upon occurrence in the network. To prevent cascading failures, we identify some dangerous stress mechanisms, based on which we quantify the robustness of the network in terms of the resource configuration scheme. Afterward, we identify the optimal resource configuration under two resource/load fluctuations scenarios: uniform and proportional fluctuations. We further investigate the optimal resource configuration problem considering heterogeneous resource sharing costs among the nodes. To mitigate/confine ongoing cascading failures, we propose two network adaptations mechanisms: intentional failure and resource re-adjustment, based on which we propose an algorithm to mitigate an ongoing cascading failure while reinforcing the surviving network with a high robustness to avoid further failures.