eFRADIR: An Enhanced FRAmework for DIsaster Resilience

This paper focuses on how to increase the availability of a backbone network with minimal cost. In particular, the new framework focuses on resilience against natural disasters and is an evolution of the FRADIR/FRADIR-II framework. It targets three different directions, namely: network planning, failure modeling, and survivable routing. The steady state network planning is tackled by upgrading a sub-network (a set of links termed the spine) to achieve the targeted availability threshold. A new two-stage approach is proposed: a heuristic algorithm combined with a mixed-integer linear problem to optimize the availability upgrade cost. To tackle the disaster-resilient network planning problem, a new integer linear program is presented for the optimal link intensity tolerance upgrades together with an efficient heuristic scheme to reduce the running time. Failure modeling is improved by considering more realistic disasters. In particular, we focus on earthquakes using the historical data of the epicenters and the moment magnitudes. The joint failure probabilities of the multi-link failures are estimated, and the set of shared risk link groups is defined. The survivable routing aims to improve the network's connectivity during these shared risk link group failures. Here, a generalized dedicated protection algorithm is used to protect against all the listed failures. Finally, the experimental results demonstrate the benefits of the refined eFRADIR framework in the event of disasters by guaranteeing low disconnection probabilities even during large-scale natural disasters.

Automated summary

This paper introduces a new framework focusing on resilience against natural disasters, targeting network planning, failure modeling, and survivable routing. It proposes a two-stage approach to optimize availability upgrade cost by upgrading a sub-network to achieve the targeted availability threshold. The framework also includes a new integer linear program for disaster-resilient network planning and an efficient heuristic scheme to reduce running time.