Network disaster recovery via dynamic service-aware risk management

Abstract

Communication networks are increasingly exposed to natural and human-induced disasters, which result in service disruption and economic losses. Most existing disaster recovery frameworks treat risk as a static factor, overlooking the fact that hazard conditions evolve over time. For example, in earthquakes, failures propagate outward from the epicenter across successive time steps, creating time-varying and correlated risk for network links. In practice, routing must therefore be both dynamic and service-aware, reflecting class priorities, (Service Level Agreement) SLA targets, and capacity constraints, yet existing models rarely integrate these elements in a unified optimization framework. This thesis formulates dynamic, service-aware routing under disaster conditions as a multi-objective mixed-integer nonlinear program (MINLP). The model jointly optimizes five objectives: maximizing revenue and survivability, while minimizing service-aware risk, overutilization cost, and SLA penalties, subject to capacity and service-level constraints. A dynamic risk model is introduced that considers time-varying link failure probabilities and is weighted by service priorities. This includes both hazard progress and the importance of impacted services. SLA compliance is preserved by class of service availability constraints, which guarantee the preservation of high-priority traffic during disruptions. We solve the model using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which approximates the Pareto front and provides a decision-support layer for service providers. Experiments are conducted on real-world network topologies (ERnet, France, Giul39) under staged and dynamic failure scenarios. The results show that adaptive routing shifts flows from high-risk to safer links as hazards evolve, improving survivability and reducing SLA penalties while explicitly exposing trade-offs with revenue and overutilization. Sensitivity analyses show how link capacity, service request volume, and failure probability affect the balance among the five objectives. By embedding dynamic risk, service differentiation, and capacity constraints in a single multi-objective formulation, this work enables decision-makers to explore balanced recovery strategies rather than commit to a single prescriptive solution. The approach provides a practical tool for evaluating disaster-resilient routing policies under uncertainty, which helps strengthen communication networks against evolving natural and human-induced disruptions.