Robust Network Routing under Cascading Failures

TL;DR

This paper introduces a new dynamical model for cascading failures in network flows, analyzing how capacity reductions and routing policies influence the resilience and failure patterns of the network.

Contribution

It presents a novel model capturing state-dependent and adversarial disturbances, along with an algorithm to estimate the network's resilience margin under local information routing policies.

Findings

The pattern of failures can be non-adjacent links, differing from standard models.

An algorithm to compute an upper bound on the network's resilience margin.

Conditions under which the resilience bound is tight with fast routing updates.

Abstract

We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, disturbance process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but are oblivious to the presence of disturbance. Under the proposed dynamics, a link becomes irreversibly inactive either due to overload condition on itself or on all of its immediate downstream links. The coupling between link activation and flow dynamics implies that links to become inactive successively are not necessarily adjacent to each other, and hence the pattern of cascading failure under our model is qualitatively different than standard cascade models. The magnitude of a disturbance process is defined as the sum of cumulative capacity reductions across time and links of the network, and the margin of resilience of the network is defined as the infimum over the magnitude of all disturbance processes under which the links at the origin node become inactive. We propose an algorithm to compute an upper bound on the margin of resilience for the setting where the routing policy only has access to information about the local state of the network. For the limiting case when the routing policies update their action as fast as network dynamics, we identify sufficient conditions on network parameters under which the upper bound is tight under an appropriate routing policy. Our analysis relies on making connections between network parameters and monotonicity in network state evolution under proposed dynamics.