Multi-Objective Optimization for Managing Disruption Risk in SDN

This paper presents a novel multi-objective optimization (MOO) approach for managing risk in Software-Defined Networks (SDN) failure while concurrently considering operational cost and risk. Unlike traditional algorithms, we introduce an Ant Colony Optimization (ACO) algorithm adept at providing a range of nondominated solutions for path selection. Essential modifications have been made to promote efficient exploration, including penalizing the last link in a failed path and updating the pheromone matrix considering two objectives. Empirical testing with the ERNet network topology demonstrated the effectiveness of the proposed algorithm, identifying numerous nondominated and knee-point solutions. This work significantly contributes to network path optimization, providing diverse options for decision-makers, thereby enhancing the adaptability and dynamic management of traffic for large-scale failure scenarios in SDN.